Scanpy Analysis¶
%config InlineBackend.figure_formats = ["retina"] # increase resolution on retina screens
import numpy as np
import pandas as pd
import warnings
import sys
warnings.filterwarnings("ignore")
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
#import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.colors
import scanpy as sc
import seaborn as sns
import anndata as ad
sc.settings.verbosity = 3 # verbosity: errors (0), warnings (1), info (2), hints (3)
sc.logging.print_header()
sc.settings.n_jobs = 23
scanpy==1.9.1 anndata==0.9.2 umap==0.5.3 numpy==1.23.5 scipy==1.11.1 pandas==1.5.2 scikit-learn==1.3.0 statsmodels==0.14.0 python-igraph==0.10.6 pynndescent==0.5.10
from gtfparse import read_gtf
%load_ext rpy2.ipython
INFO:rpy2.situation:cffi mode is CFFI_MODE.ANY INFO:rpy2.situation:R home found: /scratch/prj/sangrithi_lab/programs/miniconda3/envs/MS_Conda5/lib/R INFO:rpy2.situation:R library path: INFO:rpy2.situation:LD_LIBRARY_PATH: INFO:rpy2.rinterface_lib.embedded:Default options to initialize R: rpy2, --quiet, --no-save INFO:rpy2.rinterface_lib.embedded:R is already initialized. No need to initialize.
pd.set_option('display.max_columns', 500)
pd.set_option('display.max_rows', 100)#None
plt.rcParams.update({})
# Say, "the default sans-serif font is COMIC SANS"
matplotlib.rcParams['font.sans-serif'] = "Arial"
# Then, "ALWAYS use sans-serif fonts"
matplotlib.rcParams['font.family'] = "sans-serif"
sc.settings.set_figure_params(dpi=125, dpi_save=300, format="pdf", frameon=False)
sc.settings.figdir = '/users/k2142342/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_17Jul23/Figures/'
# Set context to `"paper"`
sns.set_context("paper", font_scale=1, rc={"font.size":16,"axes.labelsize":16,"axes.titlesize":20})
heatcolors_wr = matplotlib.colors.LinearSegmentedColormap.from_list("", ["white", "#FFF5F0", "#FEE0D2", "#FCBBA1",
"#FC9272", "#FB6A4A", "#EF3B2C",
"#CB181D", "#A50F15", "#67000D"])
heatcolors = matplotlib.colors.LinearSegmentedColormap.from_list("", ["gray", "#000004FF", "#330A5FFF", "#781C6DFF",
"#BB3754FF", "#ED6925FF",
"#FCB519FF", "#FCFFA4FF"])
#%%R
#heatcolour_wr <- colorRampPalette(c("white", "#FFF5F0", "#FEE0D2", "#FCBBA1","#FC9272", "#FB6A4A", "#EF3B2C","#CB181D", "#A50F15", "#67000D"))(100)
#show_col(heatcolour_wr)
%%R
require(tidyverse)
require(dplyr)
require(tidyr)
options(mc.cores = 23L)
require(magrittr)
#library(scatterpie)
require(extrafont)
require(scales)
require(viridis)
require(viridisLite)
require(gridExtra)
options(dplyr.width = 200)
#options(repr.matrix.max.cols=500, repr.matrix.max.rows=100)
WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Loading required package: tidyverse
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WARNING:rpy2.rinterface_lib.callbacks:R[write to console]:
Attaching package: ‘magrittr’
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set_names
WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: The following object is masked from ‘package:tidyr’:
extract
WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Loading required package: extrafont
WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Registering fonts with R
WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Loading required package: scales
WARNING:rpy2.rinterface_lib.callbacks:R[write to console]:
Attaching package: ‘scales’
WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: The following object is masked from ‘package:purrr’:
discard
WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: The following object is masked from ‘package:readr’:
col_factor
WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Loading required package: viridis
WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Loading required package: viridisLite
WARNING:rpy2.rinterface_lib.callbacks:R[write to console]:
Attaching package: ‘viridis’
WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: The following object is masked from ‘package:scales’:
viridis_pal
WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Loading required package: gridExtra
WARNING:rpy2.rinterface_lib.callbacks:R[write to console]:
Attaching package: ‘gridExtra’
WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: The following object is masked from ‘package:dplyr’:
combine
## Load the Mouse GTF file
gtf_df = read_gtf("/users/k2142342/Lab_Sangrithi/reference/annot/Mus_musculus.GRCm38/Mus_musculus.GRCm38.102.chr.gtf")
INFO:root:Extracted GTF attributes: ['gene_id', 'gene_version', 'gene_name', 'gene_source', 'gene_biotype', 'havana_gene', 'havana_gene_version', 'transcript_id', 'transcript_version', 'transcript_name', 'transcript_source', 'transcript_biotype', 'havana_transcript', 'havana_transcript_version', 'tag', 'transcript_support_level', 'exon_number', 'exon_id', 'exon_version', 'ccds_id', 'protein_id', 'protein_version']
genes = gtf_df[gtf_df.transcript_biotype.isin(['protein_coding',
'antisense', 'lincRNA',
'TR_V_gene',
'TR_V_pseudogene', 'TR_D_gene', 'TR_J_gene', 'TR_C_gene',
'TR_J_pseudogene', 'IG_LV_gene', 'IG_V_gene', 'IG_V_pseudogene',
'IG_J_gene', 'IG_C_gene', 'IG_C_pseudogene',
'IG_D_gene', 'IG_D_pseudogene', 'IG_pseudogene'])].gene_id.unique()
len(genes)
31434
genes_df = gtf_df[gtf_df.transcript_biotype.isin(['protein_coding',
'antisense', 'lincRNA',
'TR_V_gene',
'TR_V_pseudogene', 'TR_D_gene', 'TR_J_gene', 'TR_C_gene',
'TR_J_pseudogene', 'IG_LV_gene', 'IG_V_gene', 'IG_V_pseudogene',
'IG_J_gene', 'IG_C_gene', 'IG_C_pseudogene',
'IG_D_gene', 'IG_D_pseudogene', 'IG_pseudogene'])].reset_index(drop = True)
genes_df
| seqname | source | feature | start | end | score | strand | frame | gene_id | gene_version | gene_name | gene_source | gene_biotype | havana_gene | havana_gene_version | transcript_id | transcript_version | transcript_name | transcript_source | transcript_biotype | havana_transcript | havana_transcript_version | tag | transcript_support_level | exon_number | exon_id | exon_version | ccds_id | protein_id | protein_version | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | ensembl_havana | transcript | 3214482 | 3671498 | NaN | - | 0 | ENSMUSG00000051951 | 5 | Xkr4 | ensembl_havana | protein_coding | OTTMUSG00000026353 | 2 | ENSMUST00000070533 | 4 | Xkr4-201 | ensembl_havana | protein_coding | OTTMUST00000065166 | 1 | CCDS,basic | 1 | CCDS14803 | |||||
| 1 | 1 | ensembl_havana | exon | 3670552 | 3671498 | NaN | - | 0 | ENSMUSG00000051951 | 5 | Xkr4 | ensembl_havana | protein_coding | OTTMUSG00000026353 | 2 | ENSMUST00000070533 | 4 | Xkr4-201 | ensembl_havana | protein_coding | OTTMUST00000065166 | 1 | CCDS,basic | 1 | 1 | ENSMUSE00000485541 | 3 | CCDS14803 | ||
| 2 | 1 | ensembl_havana | CDS | 3670552 | 3671348 | NaN | - | 0 | ENSMUSG00000051951 | 5 | Xkr4 | ensembl_havana | protein_coding | OTTMUSG00000026353 | 2 | ENSMUST00000070533 | 4 | Xkr4-201 | ensembl_havana | protein_coding | OTTMUST00000065166 | 1 | CCDS,basic | 1 | 1 | CCDS14803 | ENSMUSP00000070648 | 4 | ||
| 3 | 1 | ensembl_havana | start_codon | 3671346 | 3671348 | NaN | - | 0 | ENSMUSG00000051951 | 5 | Xkr4 | ensembl_havana | protein_coding | OTTMUSG00000026353 | 2 | ENSMUST00000070533 | 4 | Xkr4-201 | ensembl_havana | protein_coding | OTTMUST00000065166 | 1 | CCDS,basic | 1 | 1 | CCDS14803 | ||||
| 4 | 1 | ensembl_havana | exon | 3421702 | 3421901 | NaN | - | 0 | ENSMUSG00000051951 | 5 | Xkr4 | ensembl_havana | protein_coding | OTTMUSG00000026353 | 2 | ENSMUST00000070533 | 4 | Xkr4-201 | ensembl_havana | protein_coding | OTTMUST00000065166 | 1 | CCDS,basic | 1 | 2 | ENSMUSE00000449517 | 3 | CCDS14803 | ||
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1387326 | MT | insdc | stop_codon | 13552 | 13554 | NaN | - | 0 | ENSMUSG00000064368 | 1 | mt-Nd6 | insdc | protein_coding | ENSMUST00000082419 | 1 | mt-Nd6-201 | insdc | protein_coding | basic | NA | 1 | |||||||||
| 1387327 | MT | insdc | transcript | 14145 | 15288 | NaN | + | 0 | ENSMUSG00000064370 | 1 | mt-Cytb | insdc | protein_coding | ENSMUST00000082421 | 1 | mt-Cytb-201 | insdc | protein_coding | basic | NA | ||||||||||
| 1387328 | MT | insdc | exon | 14145 | 15288 | NaN | + | 0 | ENSMUSG00000064370 | 1 | mt-Cytb | insdc | protein_coding | ENSMUST00000082421 | 1 | mt-Cytb-201 | insdc | protein_coding | basic | NA | 1 | ENSMUSE00000521548 | 1 | |||||||
| 1387329 | MT | insdc | CDS | 14145 | 15288 | NaN | + | 0 | ENSMUSG00000064370 | 1 | mt-Cytb | insdc | protein_coding | ENSMUST00000082421 | 1 | mt-Cytb-201 | insdc | protein_coding | basic | NA | 1 | ENSMUSP00000081003 | 1 | |||||||
| 1387330 | MT | insdc | start_codon | 14145 | 14147 | NaN | + | 0 | ENSMUSG00000064370 | 1 | mt-Cytb | insdc | protein_coding | ENSMUST00000082421 | 1 | mt-Cytb-201 | insdc | protein_coding | basic | NA | 1 |
1387331 rows × 30 columns
#genes_df.to_csv("/users/k2142342/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_9Feb23/genes_df.csv")
#genes_df = pd.read_csv("/users/k2142342/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_9Feb23/genes_df.csv", index_col=0)
genes = genes_df.gene_id.unique()
len(genes_df.gene_id[genes_df.seqname.isin(['MT'])].unique())
13
%%R -o countdataSC
# Load the count data
countdataSC <- read.table("~/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_17Jul23/total/Total_counts_Pos_only.txt", header=TRUE, row.names=1)
#countdataSC <- read.table("~/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_17Jul23/total/Total_counts_PosandNeg.txt", header=TRUE, row.names=1)
dim(countdataSC)
[1] 55401 738
%%R
colnames(countdataSC) %>% head() %>% print()
dim(countdataSC)
colnames(countdataSC) <- gsub("\\.", "_", colnames(countdataSC))
dim(countdataSC)
[1] "E10.GFP.pos.F1.10" "E10.GFP.pos.F1.11" "E10.GFP.pos.F1.12" [4] "E10.GFP.pos.F1.13" "E10.GFP.pos.F1.14" "E10.GFP.pos.F1.15" [1] 55401 738
%%R
colnames(countdataSC) %>% head() %>% print()
#head(countdataSC)
[1] "E10_GFP_pos_F1_10" "E10_GFP_pos_F1_11" "E10_GFP_pos_F1_12" [4] "E10_GFP_pos_F1_13" "E10_GFP_pos_F1_14" "E10_GFP_pos_F1_15"
%%R -o countdataSCMeta
countdataSCMeta <- read.table("~/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_17Jul23/BAM_LIST_18Jul23_Metadata.txt",
sep = '\t',
header = T)
rownames(countdataSCMeta) <- countdataSCMeta$sample
colnames(countdataSCMeta)[1] = "Cell"
%%R
countdataSCMeta %>% group_by(age, sex) %>% tally()
# A tibble: 14 × 3 # Groups: age [7] age sex n <chr> <chr> <int> 1 E10.5 female 48 2 E10.5 male 48 3 E11.5 female 48 4 E11.5 male 48 5 E12.5 female 48 6 E12.5 male 45 7 E13.5 female 48 8 E13.5 male 48 9 E14.5 female 48 10 E14.5 male 48 11 E15.5 female 48 12 E15.5 male 72 13 E16.5 female 72 14 E16.5 male 72
%%R -o countdataSC -o obs_names -o var_names -i genes
# subset rownames
countdataSC <- countdataSC[rownames(countdataSC) %in% genes, ]
obs_names <- colnames(countdataSC)
var_names <- rownames(countdataSC)
countdataSC <- as.matrix(countdataSC)
print(dim(countdataSC[rownames(countdataSC) %in% genes, ]))
[1] 31434 738
Genome1 and 2 counts¶
%%R -o countdataSC_g1 -o countdataSC_g2
# Load the count data
#countdataSC_g1 <- read.table("~/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCount_OUT_18Jul21/E125_145_counts_genome1.txt", header=TRUE, row.names=1)
#countdataSC_g2 <- read.table("~/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCount_OUT_18Jul21/E125_145_counts_genome2.txt", header=TRUE, row.names=1)
countdataSC_g1 <- read.table("/users/k2142342/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_17Jul23/genome1/Total_counts_genome1_Pos_only.txt", header=TRUE, row.names=1)
countdataSC_g2 <- read.table("/users/k2142342/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_17Jul23/genome2/Total_counts_genome2_Pos_only.txt", header=TRUE, row.names=1)
%%R
colnames(countdataSC_g1) <- gsub("\\.", "_", colnames(countdataSC_g1))
colnames(countdataSC_g2) <- gsub("\\.", "_", colnames(countdataSC_g2))
dim(countdataSC_g1) %>% print()
dim(countdataSC_g2) %>% print()
[1] 55401 738 [1] 55401 738
%%R -o countdataSC_g1 -o obs_names_g1 -o var_names_g1 -i genes
# subset rownames
countdataSC_g1 <- countdataSC_g1[rownames(countdataSC_g1) %in% genes, ]
obs_names_g1 <- colnames(countdataSC_g1)
var_names_g1 <- rownames(countdataSC_g1)
countdataSC_g1 <- as.matrix(countdataSC_g1)
print(dim(countdataSC_g1[rownames(countdataSC_g1) %in% genes, ]))
[1] 31434 738
%%R
#head(countdataSC_g1)
colnames(countdataSC_g1) %in% rownames(countdataSCMeta) %>% table()
#colnames(countdataSC_g1)
. TRUE 738
%%R -o countdataSC_g2 -o obs_names_g2 -o var_names_g2 -i genes
# subset rownames
countdataSC_g2 <- countdataSC_g2[rownames(countdataSC_g2) %in% genes, ]
obs_names_g2 <- colnames(countdataSC_g2)
var_names_g2 <- rownames(countdataSC_g2)
countdataSC_g2 <- as.matrix(countdataSC_g2)
print(dim(countdataSC_g2[rownames(countdataSC_g2) %in% genes, ]))
[1] 31434 738
%%R
is.na(countdataSC_g1) %>% table()
is.na(countdataSC_g2) %>% table()
. FALSE 23198292
SCANPY¶
gcAnndata = sc.AnnData(X = countdataSC.T, obs = obs_names, var = var_names)
# Read in saved gcAnndata
#gcAnndata=sc.read_h5ad("/users/k2142342/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_17Jul23/gcAnndata_17Jul23.h5ad")
#gcAnndata
gcAnndata.obs.rename(columns={0:'Sample'}, inplace=True)
gcAnndata.var.rename(columns={0:'geneid'}, inplace=True)
gcAnndata.var.index = gcAnndata.var.geneid.to_list()
gcAnndata.obs.index = gcAnndata.obs.Sample.to_list()
annot = sc.queries.biomart_annotations(
"mmusculus",
["ensembl_gene_id", "start_position", "end_position", "chromosome_name", "external_gene_name"],
host="nov2020.archive.ensembl.org",
use_cache='TRUE',
).set_index("ensembl_gene_id")
gcAnndata.var[annot.columns] = annot
gcAnndata.var.index = gcAnndata.var.geneid.tolist()
gcAnndata
AnnData object with n_obs × n_vars = 738 × 31434
obs: 'Sample'
var: 'geneid', 'start_position', 'end_position', 'chromosome_name', 'external_gene_name'
gcAnndata.var_names_make_unique()
gcAnndata.n_obs
738
gcAnndata.var.chromosome_name=='MT'
ENSMUSG00000051951 False
ENSMUSG00000089699 False
ENSMUSG00000102343 False
ENSMUSG00000025900 False
ENSMUSG00000025902 False
...
ENSMUSG00000065947 True
ENSMUSG00000064363 True
ENSMUSG00000064367 True
ENSMUSG00000064368 True
ENSMUSG00000064370 True
Name: chromosome_name, Length: 31434, dtype: bool
gcAnndata.var['mt'] = gcAnndata.var.chromosome_name=='MT' # annotate the group of mitochondrial genes as 'mt'
gcAnndata.var['mt']
ENSMUSG00000051951 False
ENSMUSG00000089699 False
ENSMUSG00000102343 False
ENSMUSG00000025900 False
ENSMUSG00000025902 False
...
ENSMUSG00000065947 True
ENSMUSG00000064363 True
ENSMUSG00000064367 True
ENSMUSG00000064368 True
ENSMUSG00000064370 True
Name: mt, Length: 31434, dtype: bool
sc.pp.calculate_qc_metrics(gcAnndata, qc_vars=['mt'], percent_top=None, log1p=False, inplace=True)
gcAnndata.obs['sex'] = countdataSCMeta['sex']
gcAnndata.obs['stage'] = countdataSCMeta['age']
gcAnndata.obs['Embryo'] = countdataSCMeta['embryo']
gcAnndata.obs['stage_sex'] = gcAnndata.obs.sex.astype('str') + "_" + gcAnndata.obs.stage.astype('str')
gcAnndata.obs
| Sample | n_genes_by_counts | total_counts | total_counts_mt | pct_counts_mt | sex | stage | Embryo | stage_sex | |
|---|---|---|---|---|---|---|---|---|---|
| E10_GFP_pos_F1_10 | E10_GFP_pos_F1_10 | 8707 | 715292 | 918 | 0.128339 | female | E10.5 | F1 | female_E10.5 |
| E10_GFP_pos_F1_11 | E10_GFP_pos_F1_11 | 8473 | 1564689 | 5792 | 0.370169 | female | E10.5 | F1 | female_E10.5 |
| E10_GFP_pos_F1_12 | E10_GFP_pos_F1_12 | 8206 | 2310070 | 3992 | 0.172809 | female | E10.5 | F1 | female_E10.5 |
| E10_GFP_pos_F1_13 | E10_GFP_pos_F1_13 | 10345 | 1608779 | 6207 | 0.385821 | female | E10.5 | F1 | female_E10.5 |
| E10_GFP_pos_F1_14 | E10_GFP_pos_F1_14 | 8338 | 1320374 | 5065 | 0.383603 | female | E10.5 | F1 | female_E10.5 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| M4_GFPP_P2_B6 | M4_GFPP_P2_B6 | 5273 | 78485 | 949 | 1.209148 | male | E12.5 | M4 | male_E12.5 |
| M4_GFPP_P2_B7 | M4_GFPP_P2_B7 | 6595 | 2647587 | 22935 | 0.866260 | male | E12.5 | M4 | male_E12.5 |
| M4_GFPP_P2_B8 | M4_GFPP_P2_B8 | 5555 | 1920124 | 20754 | 1.080868 | male | E12.5 | M4 | male_E12.5 |
| M4_GFPP_P2_B9 | M4_GFPP_P2_B9 | 5391 | 1287828 | 9147 | 0.710266 | male | E12.5 | M4 | male_E12.5 |
| M4_GFPP_P2_D2 | M4_GFPP_P2_D2 | 6042 | 3026218 | 22180 | 0.732928 | male | E12.5 | M4 | male_E12.5 |
738 rows × 9 columns
gcAnndata
AnnData object with n_obs × n_vars = 738 × 31434
obs: 'Sample', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'sex', 'stage', 'Embryo', 'stage_sex'
var: 'geneid', 'start_position', 'end_position', 'chromosome_name', 'external_gene_name', 'mt', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts'
gcAnndata.obs['stage_sex'].value_counts()
female_E16.5 72 male_E16.5 71 male_E15.5 70 female_E10.5 48 male_E10.5 48 male_E11.5 48 female_E11.5 48 female_E12.5 48 female_E13.5 48 male_E13.5 48 male_E14.5 48 female_E14.5 48 female_E15.5 48 male_E12.5 45 Name: stage_sex, dtype: int64
gcAnndata.obs['sex'].head()
E10_GFP_pos_F1_10 female E10_GFP_pos_F1_11 female E10_GFP_pos_F1_12 female E10_GFP_pos_F1_13 female E10_GFP_pos_F1_14 female Name: sex, dtype: object
gcAnndata.obs['CellID'] = gcAnndata.obs.index.tolist()
gcAnndata.var['Gene'] = gcAnndata.var.index.tolist()
sc.pl.violin(gcAnndata, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt'],
jitter=0.4, multi_panel=True)
... storing 'sex' as categorical ... storing 'stage' as categorical ... storing 'Embryo' as categorical ... storing 'stage_sex' as categorical ... storing 'chromosome_name' as categorical ... storing 'external_gene_name' as categorical
sc.pp.filter_cells(gcAnndata, min_genes = 3500)
sc.pp.filter_cells(gcAnndata, max_genes = 10000)
sc.pp.filter_cells(gcAnndata, min_counts = 500000)
sc.pp.filter_genes(gcAnndata, min_cells = 5)
gcAnndata = gcAnndata[gcAnndata.obs['pct_counts_mt'] < 5, :]
filtered out 40 cells that have less than 3500 genes expressed filtered out 6 cells that have more than 10000 genes expressed filtered out 7 cells that have less than 500000 counts filtered out 10260 genes that are detected in less than 5 cells
#gcAnndata.obs.to_csv("/users/k2142342/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_17Jul23/gcAnndata_obs.csv")
# Retain same cells for allele-specific analyses
#keep = gcAnndata.obs.Sample
#keep.to_csv(".../keep.csv")
gcAnndata.layers["counts"] = gcAnndata[gcAnndata.obs_names, gcAnndata.var_names].X.copy()
gcAnndata
AnnData object with n_obs × n_vars = 681 × 21174
obs: 'Sample', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'sex', 'stage', 'Embryo', 'stage_sex', 'CellID', 'n_genes', 'n_counts'
var: 'geneid', 'start_position', 'end_position', 'chromosome_name', 'external_gene_name', 'mt', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'Gene', 'n_cells'
layers: 'counts'
sc.pl.violin(gcAnndata, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt'],
jitter=0.4, multi_panel=True)
plt.rcParams['figure.figsize'] = [3, 2.5]
svm = sns.violinplot(data=gcAnndata.obs, y="stage", x="pct_counts_mt", hue="sex", split=True, inner="quart", linewidth=1);
sns.despine(left=True)
figure = svm.get_figure()
#figure.savefig(".../mito_counts2.png", dpi=300)
plt.rcParams['figure.figsize'] = [11.5, 2.5]
svm = sns.swarmplot(data=gcAnndata.obs, x="stage", y="pct_counts_mt", hue="sex", dodge=True, s = 3, legend=True);
figure = svm.get_figure()
#figure.savefig(".../mito_counts.png", dpi=300)
plt.rcParams['figure.figsize'] = [11.5, 2.5]
svm = sns.swarmplot(data=gcAnndata.obs, x="stage", y="n_genes_by_counts", hue="sex", s = 3, dodge=True);
figure = svm.get_figure()
#figure.savefig(".../n_genes_by_counts.png", dpi=300)
plt.rcParams['figure.figsize'] = [11.5, 2.5]
svm = sns.swarmplot(data=gcAnndata.obs, x = "stage", y = "total_counts", hue = "sex", s = 3, dodge = True);
figure = svm.get_figure()
#figure.savefig(".../total_counts.png", dpi=300)
plt.rcParams['figure.figsize'] = [5, 5]
gcAnndata.raw = gcAnndata
sc.pp.normalize_total(gcAnndata, exclude_highly_expressed = True, max_fraction = 0.10, target_sum=1e4)
normalizing counts per cell The following highly-expressed genes are not considered during normalization factor computation:
[]
finished (0:00:00)
sc.pp.log1p(gcAnndata)
sc.pp.highly_variable_genes(gcAnndata, flavor='seurat_v3', n_top_genes=800)
If you pass `n_top_genes`, all cutoffs are ignored.
extracting highly variable genes
--> added
'highly_variable', boolean vector (adata.var)
'highly_variable_rank', float vector (adata.var)
'means', float vector (adata.var)
'variances', float vector (adata.var)
'variances_norm', float vector (adata.var)
gcAnndata.var['highly_variable'].value_counts()
False 20374 True 800 Name: highly_variable, dtype: int64
sc.pl.highly_variable_genes(gcAnndata)
sc.tl.pca(gcAnndata, svd_solver='arpack')
computing PCA
on highly variable genes
with n_comps=50
finished (0:00:00)
sc.pl.pca(gcAnndata, color=['stage_sex'], size=200, ec="black", linewidth=0.5, frameon=True)#
sc.pp.neighbors(gcAnndata, n_neighbors=5, n_pcs=50)
computing neighbors
using 'X_pca' with n_pcs = 50
finished: added to `.uns['neighbors']`
`.obsp['distances']`, distances for each pair of neighbors
`.obsp['connectivities']`, weighted adjacency matrix (0:00:02)
sc.tl.leiden(gcAnndata, resolution = 0.7, random_state = 55063) # Perform clustering
running Leiden clustering
finished: found 15 clusters and added
'leiden', the cluster labels (adata.obs, categorical) (0:00:00)
sc.tl.umap(gcAnndata, random_state = 55063)
computing UMAP
finished: added
'X_umap', UMAP coordinates (adata.obsm) (0:00:01)
sc.pl.umap(gcAnndata, color=['stage_sex'], size=100, ec="black", linewidth=0.5, frameon=True)
sc.pl.umap(gcAnndata, color=['stage_sex'], size=100, ec="black", linewidth=0.5, frameon=True)
sc.tl.tsne(gcAnndata, n_pcs=50, perplexity=40, random_state = 55063)
computing tSNE
using 'X_pca' with n_pcs = 50
using sklearn.manifold.TSNE
finished: added
'X_tsne', tSNE coordinates (adata.obsm) (0:00:02)
#plt.rcParams['figure.figsize'] = [5,5]
sc.pl.tsne(gcAnndata, color=['stage_sex'], size=150, ec="black", linewidth=0.5, frameon=True)#, save = '_stage_sex.png')
sc.pl.tsne(gcAnndata, color=["leiden", 'stage_sex', 'Embryo'],legend_loc="on data", size=150, edgecolor="black", linewidths=0.5, frameon=True, legend_fontsize=8, legend_fontoutline=1)
sc.pl.tsne(gcAnndata, color=['sex', 'stage', 'stage_sex'], size=150, ec="black", linewidth=0.5, frameon=True, show=False)#, save="_multi.png")
[<AxesSubplot: title={'center': 'sex'}, xlabel='tSNE1', ylabel='tSNE2'>,
<AxesSubplot: title={'center': 'stage'}, xlabel='tSNE1', ylabel='tSNE2'>,
<AxesSubplot: title={'center': 'stage_sex'}, xlabel='tSNE1', ylabel='tSNE2'>]
# colour scheme for nice plots
colours = ["#f1eef6", "#d4b9da", "#c994c7", "#df65b0", "#e7298a", "#ce1256", "#91003f",
"#eff3ff", "#c6dbef", "#9ecae1", "#6baed6", "#4292c6", "#2171b5", "#084594"]
cats = ["male_E10.5", "male_E11.5", "male_E12.5", "male_E13.5", "male_E14.5", "male_E15.5", "male_E16.5",
"female_E10.5", "female_E11.5", "female_E12.5", "female_E13.5", "female_E14.5", "female_E15.5", "female_E16.5"]
sc.pl.tsne(gcAnndata, color = ['stage_sex'], palette = colours, size = 150, ec = "black", linewidth = 0.5, frameon = True, save = '_stage_sex.pdf')
WARNING: saving figure to file /users/k2142342/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_17Jul23/Figures/tsne_stage_sex.pdf
gcAnndata.obs.Sample = gcAnndata.obs.Sample.astype('category')
sc.pl.pca(gcAnndata, color='stage_sex', size=300, ec="black", frameon=True, legend_loc="on data", legend_fontsize=4, linewidth=0.2)
sc.pl.pca(gcAnndata, color='stage_sex', size=800, ec="black", frameon=True, legend_fontsize=4, linewidth=0.2, projection='3d')
sc.pl.correlation_matrix(gcAnndata, groupby="Sample", figsize=[5,4])
sc.pl.correlation_matrix(gcAnndata, groupby="stage_sex",
figsize=[5,4])#,
#save="_stage_sex.pdf")
WARNING: dendrogram data not found (using key=dendrogram_stage_sex). Running `sc.tl.dendrogram` with default parameters. For fine tuning it is recommended to run `sc.tl.dendrogram` independently.
using 'X_pca' with n_pcs = 50
Storing dendrogram info using `.uns['dendrogram_stage_sex']`
gcAnndata_f = gcAnndata[gcAnndata.obs.sex == 'female']
gcAnndata[:,gcAnndata.var[gcAnndata.var.chromosome_name == 'X'].geneid]
View of AnnData object with n_obs × n_vars = 681 × 734
obs: 'Sample', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'sex', 'stage', 'Embryo', 'stage_sex', 'CellID', 'n_genes', 'n_counts', 'leiden'
var: 'geneid', 'start_position', 'end_position', 'chromosome_name', 'external_gene_name', 'mt', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'Gene', 'n_cells', 'highly_variable', 'highly_variable_rank', 'means', 'variances', 'variances_norm'
uns: 'log1p', 'hvg', 'pca', 'stage_sex_colors', 'neighbors', 'leiden', 'umap', 'tsne', 'leiden_colors', 'Embryo_colors', 'sex_colors', 'stage_colors', 'dendrogram_Sample', 'dendrogram_stage_sex'
obsm: 'X_pca', 'X_umap', 'X_tsne'
varm: 'PCs'
layers: 'counts'
obsp: 'distances', 'connectivities'
Xgenes = gcAnndata.var.index[gcAnndata.var.chromosome_name=="X"].to_list()
C9genes = gcAnndata.var.index[gcAnndata.var.chromosome_name=="9"].to_list()
Agenes = gcAnndata.var.index[~gcAnndata.var.chromosome_name.isin(['X', 'Y', 'mt'])].to_list()
len(Xgenes)
734
gcAnndata.obs["Chr X"] = (np.mean(gcAnndata[:, Xgenes].X, axis=1) / np.mean(gcAnndata[:, Agenes].X, axis=1)).tolist()
gcAnndata.obs["Chr 9"] = (np.mean(gcAnndata[:, C9genes].X, axis=1) / np.mean(gcAnndata[:, Agenes].X, axis=1)).tolist()
germXA_data = gcAnndata.obs["stage_sex"].to_frame().join(gcAnndata.obs["Chr 9"]).join(gcAnndata.obs["Chr X"])
#Melt the data to long format
germXA_data_melted = germXA_data.melt(id_vars="stage_sex", var_name="key", value_name="value")
germXA_data_melted.groupby(['stage_sex', 'key']).describe()
| value | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| count | mean | std | min | 25% | 50% | 75% | max | ||
| stage_sex | key | ||||||||
| female_E10.5 | Chr 9 | 47.0 | 1.012043 | 0.026427 | 0.958342 | 0.989431 | 1.009407 | 1.033937 | 1.077519 |
| Chr X | 47.0 | 0.822947 | 0.070499 | 0.640044 | 0.782758 | 0.825400 | 0.877600 | 0.962569 | |
| female_E11.5 | Chr 9 | 47.0 | 1.029672 | 0.026420 | 0.975295 | 1.012752 | 1.025092 | 1.044855 | 1.101905 |
| Chr X | 47.0 | 0.847130 | 0.064805 | 0.654526 | 0.801558 | 0.862572 | 0.890400 | 0.973839 | |
| female_E12.5 | Chr 9 | 48.0 | 0.998605 | 0.045742 | 0.895704 | 0.973150 | 1.000592 | 1.015991 | 1.169789 |
| Chr X | 48.0 | 0.889870 | 0.092939 | 0.673507 | 0.827104 | 0.886237 | 0.959413 | 1.105513 | |
| female_E13.5 | Chr 9 | 40.0 | 1.044379 | 0.044534 | 0.930172 | 1.018523 | 1.053287 | 1.073795 | 1.122464 |
| Chr X | 40.0 | 0.992893 | 0.121615 | 0.733157 | 0.909683 | 0.978785 | 1.080452 | 1.396950 | |
| female_E14.5 | Chr 9 | 48.0 | 1.051697 | 0.051178 | 0.940974 | 1.015260 | 1.054081 | 1.075947 | 1.180031 |
| Chr X | 48.0 | 1.063192 | 0.122279 | 0.873746 | 0.970958 | 1.052641 | 1.141462 | 1.377386 | |
| female_E15.5 | Chr 9 | 47.0 | 1.037858 | 0.040084 | 0.943308 | 1.011347 | 1.041363 | 1.061268 | 1.135436 |
| Chr X | 47.0 | 1.035229 | 0.102241 | 0.812545 | 0.973312 | 1.026228 | 1.083537 | 1.352903 | |
| female_E16.5 | Chr 9 | 71.0 | 1.039123 | 0.045916 | 0.907251 | 1.017328 | 1.042530 | 1.061850 | 1.247615 |
| Chr X | 71.0 | 1.040803 | 0.110789 | 0.752412 | 0.969280 | 1.046088 | 1.123013 | 1.257005 | |
| male_E10.5 | Chr 9 | 24.0 | 1.038062 | 0.061847 | 0.970524 | 1.001445 | 1.020613 | 1.040375 | 1.216107 |
| Chr X | 24.0 | 0.773014 | 0.080408 | 0.593542 | 0.736125 | 0.773389 | 0.827852 | 0.906810 | |
| male_E11.5 | Chr 9 | 44.0 | 1.015869 | 0.033405 | 0.893333 | 1.000197 | 1.016201 | 1.034641 | 1.102942 |
| Chr X | 44.0 | 0.761341 | 0.052476 | 0.663016 | 0.730005 | 0.750823 | 0.802822 | 0.925153 | |
| male_E12.5 | Chr 9 | 40.0 | 1.001168 | 0.035163 | 0.934700 | 0.982239 | 1.001831 | 1.024509 | 1.082474 |
| Chr X | 40.0 | 0.691051 | 0.046065 | 0.590866 | 0.665667 | 0.687883 | 0.720373 | 0.819208 | |
| male_E13.5 | Chr 9 | 45.0 | 0.998012 | 0.041980 | 0.886521 | 0.974844 | 0.999950 | 1.028868 | 1.079159 |
| Chr X | 45.0 | 0.712141 | 0.078149 | 0.522520 | 0.679215 | 0.709172 | 0.767981 | 0.848880 | |
| male_E14.5 | Chr 9 | 48.0 | 1.023912 | 0.027087 | 0.977528 | 1.002257 | 1.020333 | 1.043324 | 1.084641 |
| Chr X | 48.0 | 0.784864 | 0.059649 | 0.652369 | 0.745806 | 0.791983 | 0.821869 | 0.938275 | |
| male_E15.5 | Chr 9 | 65.0 | 1.004693 | 0.031430 | 0.935337 | 0.982872 | 1.003668 | 1.026745 | 1.085654 |
| Chr X | 65.0 | 0.778226 | 0.068872 | 0.613075 | 0.740555 | 0.768841 | 0.811646 | 1.118926 | |
| male_E16.5 | Chr 9 | 67.0 | 1.035236 | 0.037549 | 0.956945 | 1.009372 | 1.034423 | 1.055281 | 1.190011 |
| Chr X | 67.0 | 0.660736 | 0.048407 | 0.526992 | 0.631664 | 0.659311 | 0.690349 | 0.774454 | |
plt.rcParams["figure.figsize"]=4,4
bx = sns.violinplot(x="stage_sex", y="value",
hue="key", palette=["#0D8DB3","#C70039"],fliersize=0.1,linewidth=0.5,split=True,scale="count",inner="quartile",
data=germXA_data_melted)
bx.set(xlabel="")
bx.set_ylabel("Chromosome:Autosome ratio", fontsize=8)
bx.set_xticklabels(bx.get_xticklabels(), rotation=40, fontsize=8,horizontalalignment="right", rotation_mode="anchor", visible=True)
bx.grid(linestyle='-', linewidth='0.1')
bx.tick_params(
axis="both", # changes apply to the x-axis
which="major", # both major and minor ticks are affected
bottom=True, # ticks along the bottom edge are off
top=False, left=True, labeltop=False, # ticks along the top edge are off
labelbottom=True, pad=0.5, length=1.5)
bx.axhline(y=0.5,color='gray',linestyle='--')
bx.axhline(y=1,color='gray',linestyle='--')
plt.legend(loc="upper right", fontsize=6, frameon=False)
plt.tight_layout()
#figure = svm.get_figure()
#plt.savefig('.../X_Aratios.pdf', dpi=600)
Stats
from scipy import stats
stats.mannwhitneyu(germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('male') & germXA_data_melted['key'].isin(['Chr X'])].value,
germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('female') & germXA_data_melted['key'].isin(['Chr X'])].value)
MannwhitneyuResult(statistic=67915.0, pvalue=3.4661425085809535e-29)
stats.mannwhitneyu(germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('male') & germXA_data_melted['key'].isin(['Chr 9'])].value,
germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('female') & germXA_data_melted['key'].isin(['Chr 9'])].value)
MannwhitneyuResult(statistic=106278.0, pvalue=0.006759871068865442)
# plt.rcParams["figure.figsize"]=10,3
#p = sns.scatterplot(gcAnndata.obs.Sample, gcAnndata.obs['Chr X'], hue=gcAnndata.obs.sex, legend=False)
# plt.setp(p.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor");
stats.mannwhitneyu(germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('female_E14.5') & germXA_data_melted['key'].isin(['Chr X'])].value,
germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('male_E14.5') & germXA_data_melted['key'].isin(['Chr X'])].value)
# At E14.5, female X output is significantly different to E14.5 male
MannwhitneyuResult(statistic=3446.0, pvalue=1.3128901010109332e-06)
stats.mannwhitneyu(germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('female_E14.5') & germXA_data_melted['key'].isin(['Chr 9'])].value,
germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('male_E14.5') & germXA_data_melted['key'].isin(['Chr 9'])].value)
# At E14.5, female 9 output is NOT significantly different to E14.5 male
MannwhitneyuResult(statistic=2741.0, pvalue=0.06432346407530999)
stats.mannwhitneyu(germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('female_E14.5') & germXA_data_melted['key'].isin(['Chr X'])].value,
germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('female_E14.5') & germXA_data_melted['key'].isin(['Chr 9'])].value)
# At E14.5, female X output is NOT significantly different to E14.5 female 9 output
MannwhitneyuResult(statistic=1131.0, pvalue=0.8805938792307255)
stats.mannwhitneyu(germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('male_E14.5') & germXA_data_melted['key'].isin(['Chr X'])].value,
germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('male_E14.5') & germXA_data_melted['key'].isin(['Chr 9'])].value)
# At E14.5, male X output is significantly different to E14.5 male 9 output
MannwhitneyuResult(statistic=2465.0, pvalue=2.6222181827301973e-08)
PAGA
sc.tl.paga(gcAnndata, groups="stage_sex")
#sc.tl.paga(gcAnndata, groups="stage_sex_celltype")
running PAGA
finished: added
'paga/connectivities', connectivities adjacency (adata.uns)
'paga/connectivities_tree', connectivities subtree (adata.uns) (0:00:00)
sc.pl.paga(gcAnndata,
threshold = 0.1561,
fontsize=10,
fontoutline=1,
layout='fa',
save='_PAGA.pdf')
WARNING: Package 'fa2' is not installed, falling back to layout 'fr'.To use the faster and better ForceAtlas2 layout, install package 'fa2' (`pip install fa2`). --> added 'pos', the PAGA positions (adata.uns['paga']) WARNING: saving figure to file /users/k2142342/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_17Jul23/Figures/paga_PAGA.pdf
gcAnndata.uns['iroot'] = np.flatnonzero(gcAnndata.obs["stage_sex"] == "male_E10.5")[0]
sc.tl.dpt(gcAnndata,
n_dcs = 15,
n_branchings = 2)
computing Diffusion Pseudotime using n_dcs=15
this uses a hierarchical implementation
WARNING: shifting branching point away from maximal kendall-tau correlation (suppress this with `allow_kendall_tau_shift=False`)
finished: added
'dpt_pseudotime', the pseudotime (adata.obs)
'dpt_groups', the branching subgroups of dpt (adata.obs)
'dpt_order', cell order (adata.obs) (0:00:00)
sc.pl.tsne(gcAnndata, color=["dpt_pseudotime", "stage_sex"], size=200,frameon=True, ec="black", linewidth=0.4)
sc.pl.tsne(gcAnndata, color=["dpt_pseudotime"], size=200,frameon=True, ec="black", linewidth=0.4)#, save = '_pseudotime.pdf')
DE gene and marker analysis¶
sc.pl.matrixplot(gcAnndata, var_names=["Eif2s3y", "Ddx3y", "Uty", "Usp9y", "Uba1y", "Kdm5d", "Sly", "Zfy2"],
gene_symbols="external_gene_name",
cmap=heatcolors_wr,
groupby="stage_sex",
#dendrogram = True,
swap_axes = True,
standard_scale="var"),
#save = '.../_Ygenes.png')
(None,)
#gcAnndata.var
sc.pl.tsne(gcAnndata,
color=["Tfap2c", "Prdm14", "Pou5f1", "Nanog", "Dnmt3a", "Dnmt3l", "Dnmt3b", "Dnmt3c", "Tet1", "Tet2", "Lefty1", "Lefty2", "Etv1",
"Pitx2", "Hesx1", "Egr4", "Phlda2", "Gata2", "Rec8", "Stra8", "Prdm9", "Spo11", "Atr", "Atm"],
gene_symbols = 'external_gene_name',
use_raw=True,
legend_fontsize=6,
size=60,
edgecolor="black",
linewidths=0.1,
wspace=0.05,
hspace=0.2,
cmap=heatcolors,
ncols=7)#,
#save="_male_female_candidategenes")
#gcAnndata.var
sc.pl.tracksplot(gcAnndata, var_names=["Tfap2c", "Prdm14", "Pou5f1", "Nanog",
"Dnmt3a", "Dnmt3l", "Dnmt3b", "Dnmt3c", "Tet1", "Tet2",
"Lefty1", "Lefty2", "Etv1", "Pitx2", "Hesx1", "Egr4",
"Phlda2", "Gata2", "Rec8", "Stra8", "Prdm9", "Spo11",
"Atr", "Atm", "Msh6", "Aurka"],
gene_symbols = 'external_gene_name',
use_raw=False,
dendrogram=False,
groupby='stage_sex',
#save = "tracksplot_malefemale.pdf",
)
%%R -o Ezh2_derep_X
Ezh2_derep_X <- read.delim("~/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_17Jul23/Table_fem_chrx.txt")
Ezh2_derep_All <- read.delim("~/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_17Jul23/Table_fem.txt")
sc.pl.matrixplot(gcAnndata_f,
var_names = gcAnndata_f.var[gcAnndata_f.var.geneid.isin(Ezh2_derep_X.ensembl_gene_id)].external_gene_name,
gene_symbols="external_gene_name",
cmap=heatcolors_wr,
groupby="stage_sex",
#dendrogram = True,
swap_axes = True,
standard_scale="var",
figsize = [3,8], save="_Ezh2_derep_X.pdf")
WARNING: saving figure to file /users/k2142342/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_17Jul23/Figures/matrixplot__Ezh2_derep_X.pdf
sc.tl.rank_genes_groups(gcAnndata, groupby="stage_sex",
groups=["female_E16.5", "female_E15.5", "female_E14.5", "female_E13.5", "female_E12.5", "female_E11.5", "female_E10.5"],
reference = 'rest',
method='wilcoxon',
tie_correct=True,
key_added="female",
pts=True)
ranking genes
WARNING: It seems you use rank_genes_groups on the raw count data. Please logarithmize your data before calling rank_genes_groups.
finished: added to `.uns['female']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:08)
sc.tl.rank_genes_groups(gcAnndata,
groupby="stage_sex",
groups=["male_E16.5", "male_E15.5", "male_E14.5", "male_E13.5", "male_E12.5", "male_E11.5", "male_E10.5"],
reference = 'rest',
method='wilcoxon',
tie_correct=True,
key_added="male",
pts=True)
ranking genes
WARNING: It seems you use rank_genes_groups on the raw count data. Please logarithmize your data before calling rank_genes_groups.
finished: added to `.uns['male']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:06)
## Male E16.5
sc.tl.rank_genes_groups(gcAnndata, groupby="stage_sex", groups=["male_E16.5"], reference = "male_E15.5", method='wilcoxon', tie_correct=True, key_added="male", pts=True)
m_df_E16_5 = sc.get.rank_genes_groups_df(gcAnndata, group="male_E16.5", key="male", pval_cutoff=0.01, log2fc_min = 1.5, gene_symbols="external_gene_name")
m_df_E16_5[1:20]
## Male E15.5
sc.tl.rank_genes_groups(gcAnndata, groupby="stage_sex", groups=["male_E15.5"], reference = "male_E14.5", method='wilcoxon', tie_correct=True, key_added="male", pts=True)
m_df_E15_5 = sc.get.rank_genes_groups_df(gcAnndata, group="male_E15.5", key="male", pval_cutoff=0.01, log2fc_min = 1.5, gene_symbols="external_gene_name")
m_df_E15_5[1:20]
## Male E14.5
sc.tl.rank_genes_groups(gcAnndata, groupby="stage_sex", groups=["male_E14.5"], reference = "male_E13.5", method='wilcoxon', tie_correct=True, key_added="male", pts=True)
m_df_E14_5 = sc.get.rank_genes_groups_df(gcAnndata, group="male_E14.5", key="male", pval_cutoff=0.01, log2fc_min = 1.5, gene_symbols="external_gene_name")
m_df_E14_5[1:20]
## Male E13.5
sc.tl.rank_genes_groups(gcAnndata, groupby="stage_sex", groups=["male_E13.5"], reference = "male_E12.5", method='wilcoxon', tie_correct=True, key_added="male", pts=True)
m_df_E13_5 = sc.get.rank_genes_groups_df(gcAnndata, group="male_E13.5", key="male", pval_cutoff=0.01, log2fc_min = 1.5, gene_symbols="external_gene_name")
m_df_E13_5[1:20]
## Male E12.5
sc.tl.rank_genes_groups(gcAnndata, groupby="stage_sex", groups=["male_E12.5"], reference = "male_E11.5", method='wilcoxon', tie_correct=True, key_added="male", pts=True)
m_df_E12_5 = sc.get.rank_genes_groups_df(gcAnndata, group="male_E12.5", key="male", pval_cutoff=0.01, log2fc_min = 1.5, gene_symbols="external_gene_name")
m_df_E12_5[1:20]
## Male E11.5
sc.tl.rank_genes_groups(gcAnndata, groupby="stage_sex", groups=["male_E11.5"], reference = "male_E10.5", method='wilcoxon', tie_correct=True, key_added="male", pts=True)
m_df_E11_5 = sc.get.rank_genes_groups_df(gcAnndata, group="male_E11.5", key="male", pval_cutoff=0.01, log2fc_min = 1.5, gene_symbols="external_gene_name")
m_df_E11_5[1:20]
ranking genes
WARNING: It seems you use rank_genes_groups on the raw count data. Please logarithmize your data before calling rank_genes_groups.
finished: added to `.uns['male']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
WARNING: It seems you use rank_genes_groups on the raw count data. Please logarithmize your data before calling rank_genes_groups.
finished: added to `.uns['male']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
WARNING: It seems you use rank_genes_groups on the raw count data. Please logarithmize your data before calling rank_genes_groups.
finished: added to `.uns['male']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
WARNING: It seems you use rank_genes_groups on the raw count data. Please logarithmize your data before calling rank_genes_groups.
finished: added to `.uns['male']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
WARNING: It seems you use rank_genes_groups on the raw count data. Please logarithmize your data before calling rank_genes_groups.
finished: added to `.uns['male']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
WARNING: It seems you use rank_genes_groups on the raw count data. Please logarithmize your data before calling rank_genes_groups.
--> Few observations in a group for normal approximation (<=25). Lower test accuracy.
finished: added to `.uns['male']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
| names | scores | logfoldchanges | pvals | pvals_adj | external_gene_name | pct_nz_group | |
|---|---|---|---|---|---|---|---|
| 1 | ENSMUSG00000056234 | 6.565826 | 213.050735 | 5.174525e-11 | 2.406918e-07 | Ncoa4 | 1.000000 |
| 2 | ENSMUSG00000039678 | 6.559144 | 553.579590 | 5.411766e-11 | 2.406918e-07 | Tbc1d13 | 1.000000 |
| 3 | ENSMUSG00000017119 | 6.552553 | 358.968750 | 5.656180e-11 | 2.406918e-07 | Nbr1 | 1.000000 |
| 4 | ENSMUSG00000004056 | 6.527918 | 203.101013 | 6.669024e-11 | 2.406918e-07 | Akt2 | 1.000000 |
| 5 | ENSMUSG00000031976 | 6.522604 | 383.036194 | 6.909717e-11 | 2.406918e-07 | Urb2 | 0.977273 |
| 6 | ENSMUSG00000002996 | 6.501409 | 232.760284 | 7.957129e-11 | 2.406918e-07 | Hbp1 | 1.000000 |
| 7 | ENSMUSG00000020211 | 6.459280 | 220.519241 | 1.052023e-10 | 2.784441e-07 | Sf3a2 | 0.977273 |
| 8 | ENSMUSG00000005672 | 6.404060 | inf | 1.512987e-10 | 3.559553e-07 | Kit | 1.000000 |
| 9 | ENSMUSG00000030091 | 6.378636 | 359.181885 | 1.786723e-10 | 3.783207e-07 | Nup210 | 1.000000 |
| 10 | ENSMUSG00000025324 | 6.329055 | 187.015335 | 2.466673e-10 | 4.396694e-07 | Atp10a | 1.000000 |
| 11 | ENSMUSG00000032479 | 6.327239 | 748.310608 | 2.495876e-10 | 4.396694e-07 | Map4 | 1.000000 |
| 12 | ENSMUSG00000030082 | 6.314525 | 776.716431 | 2.709929e-10 | 4.396694e-07 | Sec61a1 | 1.000000 |
| 13 | ENSMUSG00000052488 | 6.301390 | 447.765533 | 2.949888e-10 | 4.396694e-07 | Cherp | 1.000000 |
| 14 | ENSMUSG00000036057 | 6.292960 | 283.085724 | 3.114688e-10 | 4.396694e-07 | Ptpn23 | 0.977273 |
| 15 | ENSMUSG00000033257 | 6.274337 | 327.267731 | 3.511266e-10 | 4.646722e-07 | Ttll4 | 1.000000 |
| 16 | ENSMUSG00000020536 | 6.260870 | 350.192352 | 3.828347e-10 | 4.768319e-07 | Llgl1 | 1.000000 |
| 17 | ENSMUSG00000028454 | 6.222013 | 172.675293 | 4.908154e-10 | 5.612534e-07 | Pigo | 1.000000 |
| 18 | ENSMUSG00000032637 | 6.217970 | 480.843689 | 5.036278e-10 | 5.612534e-07 | Atxn2l | 1.000000 |
| 19 | ENSMUSG00000025153 | 6.179469 | inf | 6.431770e-10 | 6.648344e-07 | Fasn | 1.000000 |
## Female E16.5
sc.tl.rank_genes_groups(gcAnndata, groupby="stage_sex", groups=["female_E16.5"], reference = "female_E15.5", method='wilcoxon', tie_correct=True, key_added="female", pts=True)
f_df_E16_5 = sc.get.rank_genes_groups_df(gcAnndata, group="female_E16.5", key="female", pval_cutoff=0.01, log2fc_min = 1.5, gene_symbols="external_gene_name")
f_df_E16_5[1:20]
## Female E15.5
sc.tl.rank_genes_groups(gcAnndata, groupby="stage_sex", groups=["female_E15.5"], reference = "female_E14.5", method='wilcoxon', tie_correct=True, key_added="female", pts=True)
f_df_E15_5 = sc.get.rank_genes_groups_df(gcAnndata, group="female_E15.5", key="female", pval_cutoff=0.01, log2fc_min = 1.5, gene_symbols="external_gene_name")
f_df_E15_5[1:20]
## Female E14.5
sc.tl.rank_genes_groups(gcAnndata, groupby="stage_sex", groups=["female_E14.5"], reference = "female_E13.5", method='wilcoxon', tie_correct=True, key_added="female", pts=True)
f_df_E14_5 = sc.get.rank_genes_groups_df(gcAnndata, group="female_E14.5", key="female", pval_cutoff=0.01, log2fc_min = 1.5, gene_symbols="external_gene_name")
f_df_E14_5[1:20]
## Female E13.5
sc.tl.rank_genes_groups(gcAnndata, groupby="stage_sex", groups=["female_E13.5"], reference = "female_E12.5", method='wilcoxon', tie_correct=True, key_added="female", pts=True)
f_df_E13_5 = sc.get.rank_genes_groups_df(gcAnndata, group="female_E13.5", key="female", pval_cutoff=0.01, log2fc_min = 1.5, gene_symbols="external_gene_name")
f_df_E13_5[1:20]
## Female E12.5
sc.tl.rank_genes_groups(gcAnndata, groupby="stage_sex", groups=["female_E12.5"], reference = "female_E11.5", method='wilcoxon', tie_correct=True, key_added="female", pts=True)
f_df_E12_5 = sc.get.rank_genes_groups_df(gcAnndata, group="female_E12.5", key="female", pval_cutoff=0.01, log2fc_min = 1.5, gene_symbols="external_gene_name")
f_df_E12_5[1:20]
## Male E11.5
sc.tl.rank_genes_groups(gcAnndata, groupby="stage_sex", groups=["female_E11.5"], reference = "female_E10.5", method='wilcoxon', tie_correct=True, key_added="female", pts=True)
f_df_E11_5 = sc.get.rank_genes_groups_df(gcAnndata, group="female_E11.5", key="female", pval_cutoff=0.01, log2fc_min = 1.5, gene_symbols="external_gene_name")
f_df_E11_5[1:20]
ranking genes
WARNING: It seems you use rank_genes_groups on the raw count data. Please logarithmize your data before calling rank_genes_groups.
finished: added to `.uns['female']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
WARNING: It seems you use rank_genes_groups on the raw count data. Please logarithmize your data before calling rank_genes_groups.
finished: added to `.uns['female']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
WARNING: It seems you use rank_genes_groups on the raw count data. Please logarithmize your data before calling rank_genes_groups.
finished: added to `.uns['female']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
WARNING: It seems you use rank_genes_groups on the raw count data. Please logarithmize your data before calling rank_genes_groups.
finished: added to `.uns['female']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
WARNING: It seems you use rank_genes_groups on the raw count data. Please logarithmize your data before calling rank_genes_groups.
finished: added to `.uns['female']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
WARNING: It seems you use rank_genes_groups on the raw count data. Please logarithmize your data before calling rank_genes_groups.
finished: added to `.uns['female']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
| names | scores | logfoldchanges | pvals | pvals_adj | external_gene_name | pct_nz_group | |
|---|---|---|---|---|---|---|---|
| 1 | ENSMUSG00000005672 | 7.853261 | inf | 4.053579e-15 | 4.291524e-11 | Kit | 1.000000 |
| 2 | ENSMUSG00000035632 | 7.566010 | 296.673340 | 3.848639e-14 | 2.716370e-10 | Cnot3 | 1.000000 |
| 3 | ENSMUSG00000024949 | 7.512884 | 631.347900 | 5.783882e-14 | 3.061698e-10 | Sf1 | 1.000000 |
| 4 | ENSMUSG00000036097 | 7.430614 | 229.695465 | 1.080946e-13 | 4.144266e-10 | Slf2 | 1.000000 |
| 5 | ENSMUSG00000016933 | 7.419645 | 474.032745 | 1.174346e-13 | 4.144266e-10 | Plcg1 | 1.000000 |
| 6 | ENSMUSG00000003435 | 7.380548 | 446.897797 | 1.576390e-13 | 4.525582e-10 | Supt5 | 1.000000 |
| 7 | ENSMUSG00000026024 | 7.365295 | 280.987885 | 1.767555e-13 | 4.525582e-10 | Als2 | 1.000000 |
| 8 | ENSMUSG00000030662 | 7.354002 | inf | 1.923597e-13 | 4.525582e-10 | Ipo5 | 1.000000 |
| 9 | ENSMUSG00000028430 | 7.286180 | 529.008667 | 3.188652e-13 | 6.143860e-10 | Nol6 | 1.000000 |
| 10 | ENSMUSG00000030082 | 7.286049 | 694.304688 | 3.191767e-13 | 6.143860e-10 | Sec61a1 | 1.000000 |
| 11 | ENSMUSG00000022443 | 7.233193 | 470.779022 | 4.717680e-13 | 8.324346e-10 | Myh9 | 1.000000 |
| 12 | ENSMUSG00000024740 | 7.202788 | inf | 5.899351e-13 | 9.608681e-10 | Ddb1 | 1.000000 |
| 13 | ENSMUSG00000004207 | 7.172540 | 615.570374 | 7.361867e-13 | 1.098000e-09 | Psap | 1.000000 |
| 14 | ENSMUSG00000021938 | 7.165004 | 457.088776 | 7.778410e-13 | 1.098000e-09 | Pspc1 | 1.000000 |
| 15 | ENSMUSG00000066232 | 7.104842 | 271.195984 | 1.204604e-12 | 1.549117e-09 | Ipo7 | 1.000000 |
| 16 | ENSMUSG00000015697 | 7.095524 | 301.461884 | 1.288625e-12 | 1.549117e-09 | Setdb1 | 1.000000 |
| 17 | ENSMUSG00000031386 | 7.085577 | inf | 1.384655e-12 | 1.549117e-09 | Hcfc1 | 1.000000 |
| 18 | ENSMUSG00000025261 | 7.078118 | inf | 1.461254e-12 | 1.549117e-09 | Huwe1 | 1.000000 |
| 19 | ENSMUSG00000006932 | 7.068639 | 401.898010 | 1.564605e-12 | 1.549117e-09 | Ctnnb1 | 0.978723 |
sc.pl.DotPlot(gcAnndata,
pd.concat([f_df_E11_5[1:20].names, f_df_E12_5[1:20].names, f_df_E13_5[1:20].names, f_df_E14_5[1:20].names, f_df_E15_5[1:20].names, f_df_E16_5[1:20].names,
m_df_E11_5[1:20].names, m_df_E12_5[1:20].names, m_df_E13_5[1:20].names, m_df_E14_5[1:20].names, m_df_E15_5[1:20].names, m_df_E16_5[1:20].names], axis = 0).reset_index(drop=True),
cmap=heatcolors_wr,
groupby="stage_sex",
#swap_axes = False,
#dendrogram = False,
standard_scale = "var").add_totals().show()
GO_f11_5 = sc.queries.enrich(f_df_E11_5.dropna(axis=0)["names"].to_list(), org="mmusculus") GO_f12_5 = sc.queries.enrich(f_df_E12_5.dropna(axis=0)["names"].to_list(), org="mmusculus") GO_f13_5 = sc.queries.enrich(f_df_E13_5.dropna(axis=0)["names"].to_list(), org="mmusculus") GO_f14_5 = sc.queries.enrich(f_df_E14_5.dropna(axis=0)["names"].to_list(), org="mmusculus") GO_f15_5 = sc.queries.enrich(f_df_E15_5.dropna(axis=0)["names"].to_list(), org="mmusculus") GO_f16_5 = sc.queries.enrich(f_df_E16_5.dropna(axis=0)["names"].to_list(), org="mmusculus")
GO_m11_5 = sc.queries.enrich(m_df_E11_5.dropna(axis=0)["names"].to_list(), org="mmusculus") GO_m12_5 = sc.queries.enrich(m_df_E12_5.dropna(axis=0)["names"].to_list(), org="mmusculus") GO_m13_5 = sc.queries.enrich(m_df_E13_5.dropna(axis=0)["names"].to_list(), org="mmusculus") GO_m14_5 = sc.queries.enrich(m_df_E14_5.dropna(axis=0)["names"].to_list(), org="mmusculus") GO_m15_5 = sc.queries.enrich(m_df_E15_5.dropna(axis=0)["names"].to_list(), org="mmusculus") GO_m16_5 = sc.queries.enrich(m_df_E16_5.dropna(axis=0)["names"].to_list(), org="mmusculus")
Male v Female gslist¶
sc.tl.rank_genes_groups(gcAnndata, 'stage_sex', method='wilcoxon')
ranking genes
WARNING: It seems you use rank_genes_groups on the raw count data. Please logarithmize your data before calling rank_genes_groups.
finished: added to `.uns['rank_genes_groups']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:01)
sc.pl.rank_genes_groups(gcAnndata, n_genes=25, sharey=False, gene_symbols="external_gene_name")
result = gcAnndata.uns['rank_genes_groups']
groups = result['names'].dtype.names
pd.DataFrame(
{group + '_' + key[:1]: result[key][group]
for group in groups for key in ['names', 'scores', 'logfoldchanges', 'pvals_adj']}).head(50)
| female_E10.5_n | female_E10.5_s | female_E10.5_l | female_E10.5_p | female_E11.5_n | female_E11.5_s | female_E11.5_l | female_E11.5_p | female_E12.5_n | female_E12.5_s | female_E12.5_l | female_E12.5_p | female_E13.5_n | female_E13.5_s | female_E13.5_l | female_E13.5_p | female_E14.5_n | female_E14.5_s | female_E14.5_l | female_E14.5_p | female_E15.5_n | female_E15.5_s | female_E15.5_l | female_E15.5_p | female_E16.5_n | female_E16.5_s | female_E16.5_l | female_E16.5_p | male_E10.5_n | male_E10.5_s | male_E10.5_l | male_E10.5_p | male_E11.5_n | male_E11.5_s | male_E11.5_l | male_E11.5_p | male_E12.5_n | male_E12.5_s | male_E12.5_l | male_E12.5_p | male_E13.5_n | male_E13.5_s | male_E13.5_l | male_E13.5_p | male_E14.5_n | male_E14.5_s | male_E14.5_l | male_E14.5_p | male_E15.5_n | male_E15.5_s | male_E15.5_l | male_E15.5_p | male_E16.5_n | male_E16.5_s | male_E16.5_l | male_E16.5_p | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | ENSMUSG00000086290 | 10.071825 | 356.789215 | 5.194648e-20 | ENSMUSG00000049539 | 9.802875 | 328.357483 | 2.317000e-18 | ENSMUSG00000068048 | 10.435003 | inf | 3.633613e-21 | ENSMUSG00000015053 | 8.568956 | 634.627197 | 2.211137e-13 | ENSMUSG00000009549 | 11.258389 | inf | 4.458666e-25 | ENSMUSG00000085601 | 9.520860 | inf | 5.195239e-18 | ENSMUSG00000016427 | 12.876844 | inf | 6.433672e-34 | ENSMUSG00000050621 | 7.388151 | 90.319962 | 1.050828e-09 | ENSMUSG00000091405 | 9.368430 | 121.183609 | 8.214427e-17 | ENSMUSG00000066652 | 10.129660 | inf | 8.640414e-20 | ENSMUSG00000040726 | 8.459110 | 227.576523 | 5.704808e-13 | ENSMUSG00000060985 | 11.299863 | inf | 2.317641e-25 | ENSMUSG00000017009 | 11.332695 | inf | 1.913708e-25 | ENSMUSG00000031765 | 12.796465 | inf | 6.055304e-34 |
| 1 | ENSMUSG00000022114 | 9.996903 | 161.074356 | 8.323366e-20 | ENSMUSG00000091405 | 9.613456 | 166.443558 | 7.427192e-18 | ENSMUSG00000006200 | 10.269109 | inf | 1.027970e-20 | ENSMUSG00000002324 | 8.430614 | inf | 3.640409e-13 | ENSMUSG00000079184 | 11.165549 | inf | 4.622438e-25 | ENSMUSG00000079507 | 9.502802 | 79.002922 | 5.407765e-18 | ENSMUSG00000096210 | 12.460629 | inf | 5.183393e-32 | ENSMUSG00000090733 | 7.307340 | NaN | 1.442389e-09 | ENSMUSG00000039202 | 9.362884 | 286.835846 | 8.214427e-17 | ENSMUSG00000038793 | 9.385757 | inf | 6.613246e-17 | ENSMUSG00000046323 | 8.259953 | NaN | 1.315307e-12 | ENSMUSG00000015656 | 11.254965 | NaN | 2.317641e-25 | ENSMUSG00000076617 | 10.747350 | inf | 6.456875e-23 | ENSMUSG00000030720 | 12.726160 | inf | 1.119960e-33 |
| 2 | ENSMUSG00000079641 | 9.510871 | inf | 4.448272e-18 | ENSMUSG00000020422 | 9.556976 | 266.121613 | 8.557630e-18 | ENSMUSG00000006585 | 9.891661 | inf | 3.193462e-19 | ENSMUSG00000029848 | 7.799374 | inf | 4.391134e-11 | ENSMUSG00000027879 | 11.157939 | inf | 4.622438e-25 | ENSMUSG00000030041 | 9.383311 | NaN | 1.041319e-17 | ENSMUSG00000031432 | 12.366297 | inf | 1.052723e-31 | ENSMUSG00000053332 | 7.038497 | inf | 5.696867e-09 | ENSMUSG00000039678 | 9.204022 | 479.034088 | 1.413284e-16 | ENSMUSG00000063856 | 9.227533 | NaN | 1.954609e-16 | ENSMUSG00000024521 | 8.230549 | 216.246826 | 1.315307e-12 | ENSMUSG00000032399 | 11.184193 | NaN | 3.439491e-25 | ENSMUSG00000064215 | 10.645263 | inf | 1.294905e-22 | ENSMUSG00000028784 | 12.590128 | inf | 5.066626e-33 |
| 3 | ENSMUSG00000057322 | 9.343737 | inf | 1.640859e-17 | ENSMUSG00000031691 | 9.503186 | 322.096161 | 9.954063e-18 | ENSMUSG00000001270 | 9.599442 | NaN | 4.254817e-18 | ENSMUSG00000070858 | 7.443162 | 399.929077 | 5.203667e-10 | ENSMUSG00000042682 | 11.024386 | inf | 1.543055e-24 | ENSMUSG00000029848 | 9.354879 | inf | 1.225165e-17 | ENSMUSG00000031397 | 12.289491 | inf | 1.985609e-31 | ENSMUSG00000034892 | 7.024236 | NaN | 5.696867e-09 | ENSMUSG00000063021 | 9.194911 | 62.954388 | 1.413284e-16 | ENSMUSG00000030246 | 9.083806 | NaN | 5.549853e-16 | ENSMUSG00000041359 | 7.371583 | inf | 8.008315e-10 | ENSMUSG00000052374 | 11.123314 | inf | 4.512018e-25 | ENSMUSG00000058550 | 10.347286 | NaN | 2.278925e-21 | ENSMUSG00000000730 | 12.507071 | inf | 1.032946e-32 |
| 4 | ENSMUSG00000104960 | 9.299936 | 131.445847 | 2.288909e-17 | ENSMUSG00000101355 | 9.488202 | 43.106407 | 9.954063e-18 | ENSMUSG00000010760 | 9.426699 | inf | 1.792162e-17 | ENSMUSG00000007872 | 7.014052 | NaN | 9.804120e-09 | ENSMUSG00000000346 | 10.993567 | inf | 1.737889e-24 | ENSMUSG00000005470 | 9.133571 | inf | 6.378795e-17 | ENSMUSG00000078938 | 12.128869 | 756.731567 | 1.123250e-30 | ENSMUSG00000067288 | 6.969834 | NaN | 7.351964e-09 | ENSMUSG00000068457 | 9.186591 | 72.683029 | 1.413284e-16 | ENSMUSG00000054405 | 9.034102 | inf | 7.002407e-16 | ENSMUSG00000030246 | 7.340220 | NaN | 8.008315e-10 | ENSMUSG00000022234 | 11.105812 | NaN | 4.512018e-25 | ENSMUSG00000056155 | 10.201117 | 739.700562 | 8.300503e-21 | ENSMUSG00000024164 | 12.428590 | 576.699585 | 2.419528e-32 |
| 5 | ENSMUSG00000034892 | 9.241920 | NaN | 3.417679e-17 | ENSMUSG00000061991 | 9.468607 | 35.604073 | 1.000827e-17 | ENSMUSG00000005566 | 9.284775 | NaN | 5.718710e-17 | ENSMUSG00000057762 | 6.588669 | 23.610441 | 1.566121e-07 | ENSMUSG00000050608 | 10.972259 | inf | 1.833594e-24 | ENSMUSG00000061232 | 9.124734 | inf | 6.620344e-17 | ENSMUSG00000051592 | 11.921399 | 721.021729 | 1.077026e-29 | ENSMUSG00000025362 | 6.914375 | NaN | 7.351964e-09 | ENSMUSG00000051391 | 9.163614 | 524.181458 | 1.413284e-16 | ENSMUSG00000026520 | 8.991854 | inf | 8.578802e-16 | ENSMUSG00000035557 | 7.313953 | 454.106567 | 8.008315e-10 | ENSMUSG00000000902 | 11.085265 | inf | 4.512018e-25 | ENSMUSG00000052374 | 10.068204 | inf | 2.694744e-20 | ENSMUSG00000000739 | 12.419762 | 636.991089 | 2.542995e-32 |
| 6 | ENSMUSG00000069309 | 9.221172 | 74.217354 | 3.889006e-17 | ENSMUSG00000039678 | 9.452085 | 485.393005 | 1.004694e-17 | ENSMUSG00000095180 | 9.183945 | NaN | 1.143545e-16 | ENSMUSG00000048450 | 6.485534 | 365.052887 | 2.674526e-07 | ENSMUSG00000028618 | 10.939536 | inf | 2.255945e-24 | ENSMUSG00000011349 | 9.109365 | inf | 7.310792e-17 | ENSMUSG00000027855 | 11.702137 | inf | 1.253128e-28 | ENSMUSG00000108414 | 6.913319 | 544.890747 | 7.351964e-09 | ENSMUSG00000066232 | 9.162029 | 358.561188 | 1.413284e-16 | ENSMUSG00000060461 | 8.710612 | NaN | 9.142230e-15 | ENSMUSG00000032346 | 7.311209 | 143.366837 | 8.008315e-10 | ENSMUSG00000025613 | 11.078416 | NaN | 4.512018e-25 | ENSMUSG00000026354 | 10.011194 | 548.569092 | 4.116838e-20 | ENSMUSG00000020799 | 12.392293 | 459.406097 | 3.245618e-32 |
| 7 | ENSMUSG00000009927 | 9.105907 | NaN | 9.056999e-17 | ENSMUSG00000011256 | 9.206187 | 482.425598 | 8.234448e-17 | ENSMUSG00000028640 | 9.179759 | 718.805115 | 1.143545e-16 | ENSMUSG00000031146 | 6.097429 | inf | 1.956871e-06 | ENSMUSG00000047260 | 10.905293 | inf | 2.878165e-24 | ENSMUSG00000030760 | 9.096302 | 333.317230 | 7.692398e-17 | ENSMUSG00000071103 | 11.526855 | 541.904663 | 7.305648e-28 | ENSMUSG00000009927 | 6.908566 | NaN | 7.351964e-09 | ENSMUSG00000026276 | 9.156879 | 37.850594 | 1.413284e-16 | ENSMUSG00000054766 | 8.631500 | NaN | 1.602667e-14 | ENSMUSG00000066705 | 7.163016 | 653.171387 | 1.671072e-09 | ENSMUSG00000039756 | 11.068904 | inf | 4.512018e-25 | ENSMUSG00000028378 | 9.591575 | 489.576355 | 2.296073e-18 | ENSMUSG00000059625 | 12.198709 | inf | 2.907173e-31 |
| 8 | ENSMUSG00000061983 | 9.075554 | NaN | 1.140374e-16 | ENSMUSG00000031523 | 9.196582 | 197.717590 | 8.234448e-17 | ENSMUSG00000015176 | 9.151983 | inf | 1.315088e-16 | ENSMUSG00000022884 | 6.078790 | inf | 1.972324e-06 | ENSMUSG00000051159 | 10.873331 | inf | 3.633857e-24 | ENSMUSG00000022429 | 9.027143 | 616.149963 | 1.286589e-16 | ENSMUSG00000020059 | 11.484469 | inf | 1.152905e-27 | ENSMUSG00000092203 | 6.890607 | inf | 7.351964e-09 | ENSMUSG00000031523 | 9.144202 | 295.277374 | 1.413284e-16 | ENSMUSG00000059552 | 8.557773 | inf | 2.471716e-14 | ENSMUSG00000047407 | 7.148118 | 376.947021 | 1.693443e-09 | ENSMUSG00000035960 | 11.060153 | inf | 4.512018e-25 | ENSMUSG00000044595 | 9.421871 | NaN | 1.042514e-17 | ENSMUSG00000025401 | 12.137234 | 732.697937 | 5.747749e-31 |
| 9 | ENSMUSG00000074578 | 9.015616 | 466.001648 | 1.763852e-16 | ENSMUSG00000051391 | 9.182366 | 451.175049 | 8.234448e-17 | ENSMUSG00000034708 | 9.136764 | inf | 1.362531e-16 | ENSMUSG00000031158 | 6.000506 | inf | 2.974997e-06 | ENSMUSG00000026239 | 10.835662 | inf | 4.029794e-24 | ENSMUSG00000051159 | 9.015616 | inf | 1.337144e-16 | ENSMUSG00000020774 | 11.233019 | 228.363022 | 1.449741e-26 | ENSMUSG00000079641 | 6.876875 | inf | 7.479258e-09 | ENSMUSG00000049539 | 9.125583 | 278.133148 | 1.510795e-16 | ENSMUSG00000016344 | 8.556116 | inf | 2.471716e-14 | ENSMUSG00000063856 | 6.905052 | NaN | 8.855144e-09 | ENSMUSG00000019139 | 11.052543 | NaN | 4.512018e-25 | ENSMUSG00000051316 | 9.370165 | inf | 1.533412e-17 | ENSMUSG00000018012 | 12.075758 | 488.381378 | 1.112428e-30 |
| 10 | ENSMUSG00000032249 | 8.982574 | 234.360962 | 2.074303e-16 | ENSMUSG00000045817 | 9.180829 | 105.811790 | 8.234448e-17 | ENSMUSG00000010660 | 9.090343 | inf | 1.754267e-16 | ENSMUSG00000021361 | 5.856365 | 376.572937 | 5.892703e-06 | ENSMUSG00000085601 | 10.835281 | inf | 4.029794e-24 | ENSMUSG00000022837 | 8.999479 | inf | 1.412368e-16 | ENSMUSG00000112920 | 11.097893 | 24.273617 | 6.180696e-26 | ENSMUSG00000047675 | 6.863670 | NaN | 7.479258e-09 | ENSMUSG00000011256 | 8.916013 | 527.337646 | 9.304552e-16 | ENSMUSG00000051695 | 8.534578 | inf | 2.707731e-14 | ENSMUSG00000000579 | 6.701974 | 165.935944 | 2.721161e-08 | ENSMUSG00000006273 | 11.010308 | inf | 6.560788e-25 | ENSMUSG00000024767 | 9.228966 | NaN | 4.821633e-17 | ENSMUSG00000026809 | 12.012321 | 309.631592 | 2.160693e-30 |
| 11 | ENSMUSG00000093674 | 8.928400 | NaN | 3.155766e-16 | ENSMUSG00000037857 | 9.154319 | 204.273666 | 9.652163e-17 | ENSMUSG00000051695 | 9.089582 | inf | 1.754267e-16 | ENSMUSG00000006369 | 5.785537 | inf | 8.055165e-06 | ENSMUSG00000024099 | 10.829955 | inf | 4.029794e-24 | ENSMUSG00000047220 | 8.893436 | 576.618225 | 3.358933e-16 | ENSMUSG00000005883 | 10.998141 | 636.804321 | 1.795593e-25 | ENSMUSG00000063457 | 6.835149 | NaN | 8.633094e-09 | ENSMUSG00000059436 | 8.898582 | 155.717178 | 9.378932e-16 | ENSMUSG00000018293 | 8.507241 | NaN | 3.142952e-14 | ENSMUSG00000054196 | 6.421663 | 102.897881 | 1.240920e-07 | ENSMUSG00000058550 | 10.979108 | NaN | 8.498852e-25 | ENSMUSG00000005413 | 9.209079 | inf | 5.357617e-17 | ENSMUSG00000036526 | 11.968175 | 676.047119 | 3.346984e-30 |
| 12 | ENSMUSG00000101972 | 8.775867 | 144.227432 | 1.025991e-15 | ENSMUSG00000060206 | 9.112823 | 239.524078 | 1.307340e-16 | ENSMUSG00000037169 | 8.823618 | inf | 1.804993e-15 | ENSMUSG00000096472 | 5.708081 | 495.721375 | 1.083007e-05 | ENSMUSG00000051455 | 10.828814 | inf | 4.029794e-24 | ENSMUSG00000024786 | 8.891515 | 554.625244 | 3.358933e-16 | ENSMUSG00000009596 | 10.873851 | inf | 6.135619e-25 | ENSMUSG00000038900 | 6.828811 | NaN | 8.633094e-09 | ENSMUSG00000026421 | 8.896602 | 132.971664 | 9.378932e-16 | ENSMUSG00000004864 | 8.495644 | inf | 3.206066e-14 | ENSMUSG00000017009 | 6.372658 | NaN | 1.577512e-07 | ENSMUSG00000005312 | 10.971498 | inf | 8.534280e-25 | ENSMUSG00000073102 | 9.176928 | inf | 6.261381e-17 | ENSMUSG00000054932 | 11.758242 | 270.457031 | 2.836562e-29 |
| 13 | ENSMUSG00000022048 | 8.748587 | 300.700470 | 1.270680e-15 | ENSMUSG00000063021 | 9.104754 | 73.671402 | 1.307670e-16 | ENSMUSG00000028766 | 8.717461 | inf | 4.303008e-15 | ENSMUSG00000030605 | 5.703111 | inf | 1.083007e-05 | ENSMUSG00000025066 | 10.823486 | inf | 4.029794e-24 | ENSMUSG00000062732 | 8.844256 | 525.330139 | 4.758451e-16 | ENSMUSG00000035540 | 10.715141 | 80.048630 | 3.155124e-24 | ENSMUSG00000049775 | 6.774408 | 154.561874 | 1.102075e-08 | ENSMUSG00000060126 | 8.854609 | NaN | 1.270112e-15 | ENSMUSG00000005732 | 8.479903 | inf | 3.408720e-14 | ENSMUSG00000026641 | 6.296601 | 498.625183 | 2.300722e-07 | ENSMUSG00000062382 | 10.948668 | NaN | 9.189964e-25 | ENSMUSG00000067764 | 9.051639 | 181.914978 | 1.754189e-16 | ENSMUSG00000026879 | 11.757915 | 758.078735 | 2.836562e-29 |
| 14 | ENSMUSG00000046330 | 8.726302 | NaN | 1.466212e-15 | ENSMUSG00000066232 | 8.946458 | 261.630554 | 4.717390e-16 | ENSMUSG00000004264 | 8.666095 | NaN | 6.312616e-15 | ENSMUSG00000063714 | 5.667904 | 74.832603 | 1.197312e-05 | ENSMUSG00000003824 | 10.811311 | inf | 4.295378e-24 | ENSMUSG00000020097 | 8.825430 | inf | 5.356503e-16 | ENSMUSG00000039217 | 10.540178 | 255.551529 | 1.862065e-23 | ENSMUSG00000093674 | 6.765957 | NaN | 1.121633e-08 | ENSMUSG00000040661 | 8.766264 | 215.987885 | 2.433509e-15 | ENSMUSG00000061104 | 8.425229 | 102.994530 | 4.764738e-14 | ENSMUSG00000096255 | 6.267982 | 127.499306 | 2.581298e-07 | ENSMUSG00000024312 | 10.946385 | inf | 9.189964e-25 | ENSMUSG00000000579 | 8.909114 | 242.656265 | 6.051297e-16 | ENSMUSG00000036598 | 11.732081 | 161.754013 | 3.680318e-29 |
| 15 | ENSMUSG00000092203 | 8.715160 | 482.455261 | 1.576365e-15 | ENSMUSG00000039202 | 8.944921 | 197.819107 | 4.717390e-16 | ENSMUSG00000030605 | 8.615489 | inf | 9.216473e-15 | ENSMUSG00000035781 | 5.665005 | 200.785110 | 1.197312e-05 | ENSMUSG00000031010 | 10.797994 | inf | 4.420902e-24 | ENSMUSG00000029110 | 8.802377 | NaN | 6.165693e-16 | ENSMUSG00000060771 | 10.508945 | 423.933319 | 2.410977e-23 | ENSMUSG00000022114 | 6.752753 | 251.388794 | 1.181393e-08 | ENSMUSG00000101355 | 8.759926 | 44.786339 | 2.433509e-15 | ENSMUSG00000003873 | 8.425229 | inf | 4.764738e-14 | ENSMUSG00000052374 | 6.203295 | inf | 3.403922e-07 | ENSMUSG00000029614 | 10.946005 | NaN | 9.189964e-25 | ENSMUSG00000053909 | 8.897182 | 161.731735 | 6.383706e-16 | ENSMUSG00000029223 | 11.698728 | inf | 5.168798e-29 |
| 16 | ENSMUSG00000108414 | 8.669823 | 310.604126 | 2.235114e-15 | ENSMUSG00000022443 | 8.943000 | 441.339966 | 4.717390e-16 | ENSMUSG00000029591 | 8.536727 | inf | 1.719792e-14 | ENSMUSG00000041765 | 5.378793 | 324.426788 | 4.973215e-05 | ENSMUSG00000022432 | 10.794188 | inf | 4.420902e-24 | ENSMUSG00000032854 | 8.786624 | 525.596558 | 6.526723e-16 | ENSMUSG00000045106 | 10.502572 | 220.560959 | 2.506826e-23 | ENSMUSG00000039001 | 6.729513 | inf | 1.334994e-08 | ENSMUSG00000069267 | 8.759926 | 43.575199 | 2.433509e-15 | ENSMUSG00000028945 | 8.385467 | 536.316223 | 6.293053e-14 | ENSMUSG00000038793 | 6.149977 | 812.830566 | 4.434750e-07 | ENSMUSG00000037058 | 10.930024 | inf | 1.031627e-24 | ENSMUSG00000060985 | 8.761286 | inf | 2.043664e-15 | ENSMUSG00000048997 | 11.683032 | 669.512817 | 6.102944e-29 |
| 17 | ENSMUSG00000031245 | 8.630249 | 402.964752 | 2.846467e-15 | ENSMUSG00000025278 | 8.911879 | 227.916290 | 5.902262e-16 | ENSMUSG00000021953 | 8.497156 | inf | 2.285527e-14 | ENSMUSG00000015217 | 5.378379 | 326.979675 | 4.973215e-05 | ENSMUSG00000039105 | 10.787339 | inf | 4.420902e-24 | ENSMUSG00000028212 | 8.763955 | 171.921585 | 7.676233e-16 | ENSMUSG00000036499 | 10.489824 | 286.133453 | 2.790538e-23 | ENSMUSG00000060636 | 6.602222 | NaN | 2.382306e-08 | ENSMUSG00000035203 | 8.714367 | 147.499298 | 3.439458e-15 | ENSMUSG00000016921 | 8.365170 | NaN | 7.061307e-14 | ENSMUSG00000060461 | 6.065688 | NaN | 7.133456e-07 | ENSMUSG00000041438 | 10.893877 | NaN | 1.379583e-24 | ENSMUSG00000031880 | 8.738085 | inf | 2.390609e-15 | ENSMUSG00000021681 | 11.537844 | inf | 2.951505e-28 |
| 18 | ENSMUSG00000046402 | 8.549563 | 214.926605 | 5.293363e-15 | ENSMUSG00000099583 | 8.905731 | 54.436337 | 5.910371e-16 | ENSMUSG00000029622 | 8.453780 | inf | 3.142902e-14 | ENSMUSG00000025920 | 5.267787 | 202.640869 | 7.816256e-05 | ENSMUSG00000028648 | 10.786959 | inf | 4.420902e-24 | ENSMUSG00000005683 | 8.758961 | NaN | 7.872680e-16 | ENSMUSG00000031431 | 10.410469 | inf | 6.029813e-23 | ENSMUSG00000040459 | 6.525637 | inf | 3.676312e-08 | ENSMUSG00000089774 | 8.700897 | 210.170532 | 3.669453e-15 | ENSMUSG00000055044 | 8.324164 | inf | 9.464276e-14 | ENSMUSG00000095677 | 6.054711 | 191.888718 | 7.264747e-07 | ENSMUSG00000037742 | 10.893497 | NaN | 1.379583e-24 | ENSMUSG00000045877 | 8.659199 | 139.925888 | 4.373739e-15 | ENSMUSG00000039661 | 11.498931 | 527.723938 | 4.418717e-28 |
| 19 | ENSMUSG00000032607 | 8.546490 | inf | 5.293363e-15 | ENSMUSG00000070047 | 8.893821 | 482.350708 | 6.250892e-16 | ENSMUSG00000031169 | 8.416111 | inf | 4.120339e-14 | ENSMUSG00000064367 | 5.252876 | NaN | 8.129842e-05 | ENSMUSG00000030180 | 10.778969 | inf | 4.581098e-24 | ENSMUSG00000024841 | 8.744745 | inf | 8.764478e-16 | ENSMUSG00000045193 | 10.352467 | 607.291809 | 1.066123e-22 | ENSMUSG00000090862 | 6.511376 | NaN | 3.941836e-08 | ENSMUSG00000021255 | 8.694559 | 324.045776 | 3.686182e-15 | ENSMUSG00000006442 | 8.290614 | inf | 1.188579e-13 | ENSMUSG00000026305 | 6.010802 | 309.216888 | 8.686401e-07 | ENSMUSG00000028378 | 10.875995 | inf | 1.588169e-24 | ENSMUSG00000025357 | 8.648592 | 410.290436 | 4.493540e-15 | ENSMUSG00000040701 | 11.490102 | 511.874268 | 4.819098e-28 |
| 20 | ENSMUSG00000087166 | 8.520747 | 329.701385 | 6.216030e-15 | ENSMUSG00000101972 | 8.864236 | 174.659836 | 7.766882e-16 | ENSMUSG00000060860 | 8.387955 | 374.219116 | 4.987720e-14 | ENSMUSG00000028843 | 5.201516 | 359.669159 | 9.855447e-05 | ENSMUSG00000022837 | 10.744724 | inf | 6.326898e-24 | ENSMUSG00000002109 | 8.711702 | 673.440430 | 1.111988e-15 | ENSMUSG00000112858 | 10.197263 | 466.076385 | 4.745285e-22 | ENSMUSG00000008683 | 6.488136 | NaN | 4.182098e-08 | ENSMUSG00000074578 | 8.688617 | 466.598541 | 3.699201e-15 | ENSMUSG00000056201 | 8.285230 | inf | 1.188579e-13 | ENSMUSG00000025403 | 5.989632 | 453.384766 | 9.680962e-07 | ENSMUSG00000002524 | 10.870667 | NaN | 1.603510e-24 | ENSMUSG00000032380 | 8.646604 | 238.079346 | 4.493540e-15 | ENSMUSG00000036912 | 11.464923 | inf | 6.163346e-28 |
| 21 | ENSMUSG00000085525 | 8.487320 | 15.317883 | 8.140677e-15 | ENSMUSG00000054753 | 8.841567 | 153.894287 | 9.084155e-16 | ENSMUSG00000034165 | 8.348763 | inf | 6.264826e-14 | ENSMUSG00000027239 | 5.190746 | 357.929596 | 9.855447e-05 | ENSMUSG00000040785 | 10.735973 | inf | 6.639811e-24 | ENSMUSG00000050035 | 8.629480 | 499.859344 | 2.139318e-15 | ENSMUSG00000060445 | 10.177822 | 238.629791 | 5.732462e-22 | ENSMUSG00000046330 | 6.454861 | NaN | 4.879861e-08 | ENSMUSG00000061983 | 8.637511 | NaN | 5.529365e-15 | ENSMUSG00000026305 | 8.221029 | inf | 1.941891e-13 | ENSMUSG00000025739 | 5.958269 | 22.707628 | 1.148462e-06 | ENSMUSG00000019471 | 10.866101 | NaN | 1.609160e-24 | ENSMUSG00000051965 | 8.611469 | 142.622147 | 5.874150e-15 | ENSMUSG00000049154 | 11.452170 | 150.155563 | 7.037466e-28 |
| 22 | ENSMUSG00000087590 | 8.446209 | 137.487274 | 1.117816e-14 | ENSMUSG00000026923 | 8.820820 | 250.488373 | 1.046042e-15 | ENSMUSG00000002985 | 8.347242 | inf | 6.264826e-14 | ENSMUSG00000068048 | 5.177492 | NaN | 1.035172e-04 | ENSMUSG00000029848 | 10.721895 | inf | 7.396200e-24 | ENSMUSG00000032387 | 8.622949 | inf | 2.228455e-15 | ENSMUSG00000025037 | 10.130975 | 646.860107 | 9.166689e-22 | ENSMUSG00000025290 | 6.423170 | NaN | 5.434601e-08 | ENSMUSG00000086290 | 8.624834 | 215.369080 | 5.908971e-15 | ENSMUSG00000064368 | 8.148129 | 255.045242 | 3.402525e-13 | ENSMUSG00000070436 | 5.861826 | 341.220825 | 1.837632e-06 | ENSMUSG00000019432 | 10.825769 | NaN | 2.392548e-24 | ENSMUSG00000060376 | 8.600200 | 323.075592 | 6.240622e-15 | ENSMUSG00000038080 | 11.427973 | inf | 8.785742e-28 |
| 23 | ENSMUSG00000090862 | 8.400872 | NaN | 1.617896e-14 | ENSMUSG00000069267 | 8.783550 | 43.118755 | 1.397428e-15 | ENSMUSG00000030541 | 8.345340 | inf | 6.264826e-14 | ENSMUSG00000022982 | 5.133587 | NaN | 1.203833e-04 | ENSMUSG00000030041 | 10.692597 | NaN | 9.725946e-24 | ENSMUSG00000009291 | 8.618338 | inf | 2.247520e-15 | ENSMUSG00000073411 | 9.970034 | inf | 4.197316e-21 | ENSMUSG00000058600 | 6.380388 | NaN | 6.530132e-08 | ENSMUSG00000054753 | 8.616118 | 169.961548 | 6.110630e-15 | ENSMUSG00000021377 | 8.062805 | inf | 6.577312e-13 | ENSMUSG00000025491 | 5.848888 | NaN | 1.940435e-06 | ENSMUSG00000030357 | 10.792666 | NaN | 3.123908e-24 | ENSMUSG00000074397 | 8.517006 | inf | 1.238162e-14 | ENSMUSG00000038375 | 11.370420 | 608.900269 | 1.573419e-27 |
| 24 | ENSMUSG00000067288 | 8.393956 | NaN | 1.686929e-14 | ENSMUSG00000021255 | 8.762803 | 288.813995 | 1.613073e-15 | ENSMUSG00000003528 | 8.286743 | inf | 9.857867e-14 | ENSMUSG00000079641 | 5.092581 | inf | 1.411153e-04 | ENSMUSG00000008892 | 10.678518 | inf | 1.086671e-23 | ENSMUSG00000028273 | 8.577611 | 457.123230 | 3.061187e-15 | ENSMUSG00000000563 | 9.953780 | NaN | 4.895866e-21 | ENSMUSG00000032607 | 6.379331 | inf | 6.530132e-08 | ENSMUSG00000025278 | 8.593142 | 287.445648 | 7.167252e-15 | ENSMUSG00000009013 | 8.057835 | NaN | 6.577312e-13 | ENSMUSG00000028044 | 5.801451 | 284.541351 | 2.442195e-06 | ENSMUSG00000071644 | 10.791905 | NaN | 3.123908e-24 | ENSMUSG00000032047 | 8.491484 | 460.753967 | 1.489595e-14 | ENSMUSG00000058672 | 11.143810 | 318.193573 | 1.710940e-26 |
| 25 | ENSMUSG00000047675 | 8.357071 | NaN | 2.195758e-14 | ENSMUSG00000001910 | 8.746666 | 297.883850 | 1.789603e-15 | ENSMUSG00000047945 | 8.254022 | inf | 1.247247e-13 | ENSMUSG00000048583 | 5.024652 | 7.491227 | 1.906964e-04 | ENSMUSG00000066551 | 10.674334 | NaN | 1.093047e-23 | ENSMUSG00000027879 | 8.563011 | inf | 3.264664e-15 | ENSMUSG00000089719 | 9.901514 | 18.310062 | 8.113770e-21 | ENSMUSG00000041841 | 6.375634 | NaN | 6.530132e-08 | ENSMUSG00000026773 | 8.582445 | 126.583672 | 7.563799e-15 | ENSMUSG00000042367 | 7.967953 | inf | 1.313797e-12 | ENSMUSG00000067148 | 5.768520 | inf | 2.731135e-06 | ENSMUSG00000064215 | 10.790003 | inf | 3.123908e-24 | ENSMUSG00000022652 | 8.421216 | inf | 2.544961e-14 | ENSMUSG00000025372 | 11.072851 | inf | 3.562000e-26 |
| 26 | ENSMUSG00000079259 | 8.328256 | 387.264221 | 2.757410e-14 | ENSMUSG00000032249 | 8.734756 | 172.507019 | 1.850906e-15 | ENSMUSG00000020307 | 8.233475 | inf | 1.426185e-13 | ENSMUSG00000046814 | 5.000215 | 135.967194 | 2.064037e-04 | ENSMUSG00000030417 | 10.646937 | inf | 1.413131e-23 | ENSMUSG00000051455 | 8.561858 | 228.851288 | 3.264664e-15 | ENSMUSG00000020664 | 9.898646 | 485.481781 | 8.272529e-21 | ENSMUSG00000057278 | 6.372465 | inf | 6.530132e-08 | ENSMUSG00000070047 | 8.574125 | 497.344666 | 7.675970e-15 | ENSMUSG00000021963 | 7.958841 | inf | 1.361868e-12 | ENSMUSG00000026672 | 5.741469 | 179.000793 | 3.131994e-06 | ENSMUSG00000050708 | 10.786578 | NaN | 3.123908e-24 | ENSMUSG00000020661 | 8.398346 | inf | 2.860030e-14 | ENSMUSG00000020661 | 11.067946 | inf | 3.689349e-26 |
| 27 | ENSMUSG00000053332 | 8.307123 | inf | 3.243923e-14 | ENSMUSG00000045671 | 8.730913 | 98.047829 | 1.850906e-15 | ENSMUSG00000059552 | 8.194283 | inf | 1.906336e-13 | ENSMUSG00000062683 | 4.999386 | inf | 2.064037e-04 | ENSMUSG00000000088 | 10.643514 | inf | 1.413700e-23 | ENSMUSG00000049755 | 8.549179 | inf | 3.547132e-15 | ENSMUSG00000025090 | 9.862315 | 346.131622 | 1.156653e-20 | ENSMUSG00000007836 | 6.351866 | 126.879761 | 6.815512e-08 | ENSMUSG00000061991 | 8.569768 | 49.910564 | 7.675970e-15 | ENSMUSG00000004264 | 7.894640 | NaN | 2.132901e-12 | ENSMUSG00000018899 | 5.736372 | 423.486328 | 3.131994e-06 | ENSMUSG00000068039 | 10.757280 | NaN | 4.141206e-24 | ENSMUSG00000023966 | 8.396688 | 344.606628 | 2.860030e-14 | ENSMUSG00000031825 | 11.047018 | 499.898102 | 4.500844e-26 |
| 28 | ENSMUSG00000031939 | 8.288297 | 407.572479 | 3.685612e-14 | ENSMUSG00000094338 | 8.730145 | 62.994644 | 1.850906e-15 | ENSMUSG00000020733 | 8.184772 | inf | 1.991966e-13 | ENSMUSG00000064368 | 4.988203 | 94.684120 | 2.150697e-04 | ENSMUSG00000032409 | 10.636284 | inf | 1.475072e-23 | ENSMUSG00000022940 | 8.534964 | 451.675812 | 3.958130e-15 | ENSMUSG00000043410 | 9.800170 | 450.928162 | 2.034093e-20 | ENSMUSG00000079435 | 6.343415 | NaN | 6.967496e-08 | ENSMUSG00000037857 | 8.568183 | 203.560730 | 7.675970e-15 | ENSMUSG00000036438 | 7.894226 | NaN | 2.132901e-12 | ENSMUSG00000040296 | 5.700304 | 197.916733 | 3.650971e-06 | ENSMUSG00000024966 | 10.751193 | NaN | 4.181224e-24 | ENSMUSG00000038845 | 8.360891 | NaN | 3.765714e-14 | ENSMUSG00000067338 | 11.026417 | inf | 5.387910e-26 |
| 29 | ENSMUSG00000093483 | 8.234507 | 33.229862 | 5.376978e-14 | ENSMUSG00000035621 | 8.726686 | 56.694969 | 1.850906e-15 | ENSMUSG00000018677 | 8.174118 | NaN | 2.103555e-13 | ENSMUSG00000020644 | 4.956310 | 235.792862 | 2.443664e-04 | ENSMUSG00000029148 | 10.631718 | NaN | 1.497476e-23 | ENSMUSG00000024905 | 8.505379 | 707.554016 | 4.899805e-15 | ENSMUSG00000005881 | 9.769575 | inf | 2.683607e-20 | ENSMUSG00000041453 | 6.341831 | NaN | 6.967496e-08 | ENSMUSG00000042348 | 8.530944 | 119.847816 | 1.024527e-14 | ENSMUSG00000045394 | 7.884699 | inf | 2.167894e-12 | ENSMUSG00000054612 | 5.698736 | 284.307556 | 3.650971e-06 | ENSMUSG00000030007 | 10.749671 | NaN | 4.181224e-24 | ENSMUSG00000053398 | 8.313494 | inf | 5.392634e-14 | ENSMUSG00000004032 | 11.013664 | inf | 6.153037e-26 |
| 30 | ENSMUSG00000028367 | 8.205306 | inf | 6.581219e-14 | ENSMUSG00000050271 | 8.705555 | 82.306290 | 2.158553e-15 | ENSMUSG00000002870 | 8.146342 | NaN | 2.562044e-13 | ENSMUSG00000042745 | 4.954238 | 91.233078 | 2.443664e-04 | ENSMUSG00000025480 | 10.611933 | inf | 1.749585e-23 | ENSMUSG00000025480 | 8.493084 | inf | 5.260180e-15 | ENSMUSG00000028957 | 9.768619 | inf | 2.686660e-20 | ENSMUSG00000030539 | 6.330739 | 579.062561 | 7.380343e-08 | ENSMUSG00000022443 | 8.500440 | 431.649628 | 1.290071e-14 | ENSMUSG00000058135 | 7.883871 | inf | 2.167894e-12 | ENSMUSG00000021273 | 5.693247 | NaN | 3.717247e-06 | ENSMUSG00000052833 | 10.747007 | NaN | 4.181224e-24 | ENSMUSG00000025081 | 8.311836 | 552.779358 | 5.392634e-14 | ENSMUSG00000053398 | 10.992083 | inf | 7.681351e-26 |
| 31 | ENSMUSG00000070526 | 8.162659 | 130.704010 | 8.896516e-14 | ENSMUSG00000052609 | 8.690187 | 317.689514 | 2.394289e-15 | ENSMUSG00000042367 | 8.028389 | inf | 6.534872e-13 | ENSMUSG00000009905 | 4.953825 | 129.755112 | 2.443664e-04 | ENSMUSG00000030086 | 10.611172 | inf | 1.749585e-23 | ENSMUSG00000000346 | 8.480021 | inf | 5.813932e-15 | ENSMUSG00000020132 | 9.758739 | 248.763504 | 2.913506e-20 | ENSMUSG00000012848 | 6.305387 | NaN | 8.454181e-08 | ENSMUSG00000026398 | 8.486177 | 98.802994 | 1.412998e-14 | ENSMUSG00000102252 | 7.851563 | 707.585388 | 2.718764e-12 | ENSMUSG00000053654 | 5.612879 | 354.582245 | 4.956913e-06 | ENSMUSG00000062867 | 10.719612 | NaN | 5.448874e-24 | ENSMUSG00000023092 | 8.258804 | 325.229919 | 7.988535e-14 | ENSMUSG00000023043 | 10.969520 | 315.747803 | 9.775885e-26 |
| 32 | ENSMUSG00000042699 | 8.161890 | 295.865570 | 8.896516e-14 | ENSMUSG00000058385 | 8.678275 | 76.707970 | 2.578189e-15 | ENSMUSG00000020717 | 8.009746 | inf | 7.375379e-13 | ENSMUSG00000027984 | 4.898322 | 227.383148 | 2.966161e-04 | ENSMUSG00000032065 | 10.586820 | inf | 2.201121e-23 | ENSMUSG00000026027 | 8.476947 | 409.050812 | 5.897622e-15 | ENSMUSG00000026894 | 9.713803 | 366.516449 | 4.460491e-20 | ENSMUSG00000028936 | 6.301161 | inf | 8.568923e-08 | ENSMUSG00000081683 | 8.468350 | 61.280212 | 1.587345e-14 | ENSMUSG00000015176 | 7.840380 | NaN | 2.882139e-12 | ENSMUSG00000066652 | 5.545447 | 214.730942 | 6.748148e-06 | ENSMUSG00000037958 | 10.694880 | inf | 6.901372e-24 | ENSMUSG00000038803 | 8.215053 | 140.890274 | 1.122603e-13 | ENSMUSG00000036564 | 10.944667 | 457.607666 | 1.243548e-25 |
| 33 | ENSMUSG00000030539 | 8.161121 | 303.485504 | 8.896516e-14 | ENSMUSG00000070780 | 8.668670 | 303.204315 | 2.722706e-15 | ENSMUSG00000040945 | 8.003277 | inf | 7.544870e-13 | ENSMUSG00000031311 | 4.825008 | inf | 3.849746e-04 | ENSMUSG00000019054 | 10.581493 | inf | 2.205741e-23 | ENSMUSG00000022404 | 8.432378 | inf | 8.339405e-15 | ENSMUSG00000113902 | 9.688626 | 345.136688 | 5.531998e-20 | ENSMUSG00000049517 | 6.285844 | NaN | 9.205821e-08 | ENSMUSG00000070733 | 8.465577 | 146.090942 | 1.587345e-14 | ENSMUSG00000041891 | 7.823398 | 534.219482 | 3.202030e-12 | ENSMUSG00000029447 | 5.517612 | NaN | 7.579337e-06 | ENSMUSG00000059447 | 10.687270 | inf | 7.271224e-24 | ENSMUSG00000007564 | 8.210743 | NaN | 1.135253e-13 | ENSMUSG00000029762 | 10.923412 | 494.059357 | 1.521192e-25 |
| 34 | ENSMUSG00000041841 | 8.156895 | NaN | 9.098117e-14 | ENSMUSG00000030539 | 8.659064 | 347.633911 | 2.877550e-15 | ENSMUSG00000028333 | 7.940115 | NaN | 1.222005e-12 | ENSMUSG00000054717 | 4.789802 | NaN | 4.418654e-04 | ENSMUSG00000042043 | 10.581113 | 595.976624 | 2.205741e-23 | ENSMUSG00000037315 | 8.422772 | 644.796021 | 8.875926e-15 | ENSMUSG00000034210 | 9.666955 | 524.551025 | 6.733448e-20 | ENSMUSG00000071072 | 6.273168 | inf | 9.603650e-08 | ENSMUSG00000052609 | 8.460823 | 321.274567 | 1.606175e-14 | ENSMUSG00000027405 | 7.781149 | NaN | 4.347847e-12 | ENSMUSG00000059796 | 5.505851 | NaN | 8.019694e-06 | ENSMUSG00000021037 | 10.679660 | NaN | 7.667088e-24 | ENSMUSG00000026113 | 8.118931 | 278.225769 | 2.371024e-13 | ENSMUSG00000011589 | 10.875670 | inf | 2.452028e-25 |
| 35 | ENSMUSG00000028788 | 8.150363 | inf | 9.484703e-14 | ENSMUSG00000034275 | 8.645233 | 154.899399 | 3.114477e-15 | ENSMUSG00000029249 | 7.927559 | inf | 1.309917e-12 | ENSMUSG00000037235 | 4.764121 | 148.231796 | 4.725005e-04 | ENSMUSG00000024844 | 10.571600 | NaN | 2.373539e-23 | ENSMUSG00000028955 | 8.422388 | inf | 8.875926e-15 | ENSMUSG00000022432 | 9.653889 | inf | 7.534468e-20 | ENSMUSG00000079259 | 6.252569 | 730.939697 | 1.062613e-07 | ENSMUSG00000022048 | 8.372479 | 341.714539 | 3.318341e-14 | ENSMUSG00000020358 | 7.693753 | 582.109680 | 8.283238e-12 | ENSMUSG00000060703 | 5.473703 | 88.857765 | 9.169237e-06 | ENSMUSG00000005656 | 10.671289 | inf | 8.157189e-24 | ENSMUSG00000025959 | 8.091752 | 145.843750 | 2.830224e-13 | ENSMUSG00000040752 | 10.873055 | 313.199219 | 2.504118e-25 |
| 36 | ENSMUSG00000026873 | 8.146137 | 506.340240 | 9.702192e-14 | ENSMUSG00000069302 | 8.643696 | 17.135046 | 3.114477e-15 | ENSMUSG00000024827 | 7.922612 | 815.749451 | 1.309917e-12 | ENSMUSG00000010760 | 4.737198 | 411.665436 | 5.213936e-04 | ENSMUSG00000022940 | 10.559044 | inf | 2.640087e-23 | ENSMUSG00000052525 | 8.408556 | 372.759521 | 9.765646e-15 | ENSMUSG00000030309 | 9.651977 | inf | 7.619034e-20 | ENSMUSG00000054766 | 6.245174 | NaN | 1.093116e-07 | ENSMUSG00000010660 | 8.359010 | inf | 3.595885e-14 | ENSMUSG00000033845 | 7.692096 | inf | 8.283238e-12 | ENSMUSG00000069053 | 5.463510 | 302.936310 | 9.596350e-06 | ENSMUSG00000043716 | 10.635142 | NaN | 1.170383e-23 | ENSMUSG00000025403 | 8.074184 | inf | 3.196281e-13 | ENSMUSG00000020265 | 10.838392 | inf | 3.604583e-25 |
| 37 | ENSMUSG00000038900 | 8.127695 | NaN | 1.116180e-13 | ENSMUSG00000059901 | 8.621027 | 187.587006 | 3.697427e-15 | ENSMUSG00000020349 | 7.918808 | 363.451874 | 1.309917e-12 | ENSMUSG00000034807 | 4.729328 | 354.218475 | 5.241468e-04 | ENSMUSG00000011349 | 10.552576 | inf | 2.753918e-23 | ENSMUSG00000073411 | 8.398951 | inf | 1.048195e-14 | ENSMUSG00000037363 | 9.629987 | 262.545654 | 9.300546e-20 | ENSMUSG00000059291 | 6.239364 | NaN | 1.120826e-07 | ENSMUSG00000061013 | 8.356633 | 139.606384 | 3.595885e-14 | ENSMUSG00000037169 | 7.657718 | inf | 1.054413e-11 | ENSMUSG00000032380 | 5.458414 | 151.671860 | 9.780907e-06 | ENSMUSG00000033628 | 10.629436 | inf | 1.211516e-23 | ENSMUSG00000037772 | 8.036068 | NaN | 4.270124e-13 | ENSMUSG00000041138 | 10.808963 | 44.224384 | 4.755472e-25 |
| 38 | ENSMUSG00000018102 | 8.117321 | 178.620010 | 1.201341e-13 | ENSMUSG00000014498 | 8.617570 | 124.853401 | 3.713066e-15 | ENSMUSG00000020358 | 7.918046 | inf | 1.309917e-12 | ENSMUSG00000062054 | 4.726429 | 356.474304 | 5.259039e-04 | ENSMUSG00000053012 | 10.549532 | 667.095886 | 2.771669e-23 | ENSMUSG00000057054 | 8.378588 | inf | 1.219592e-14 | ENSMUSG00000057054 | 9.610866 | inf | 1.111897e-19 | ENSMUSG00000028367 | 6.210843 | inf | 1.312819e-07 | ENSMUSG00000005672 | 8.348313 | inf | 3.759446e-14 | ENSMUSG00000005566 | 7.654404 | NaN | 1.054413e-11 | ENSMUSG00000074397 | 5.436067 | 300.242371 | 1.067903e-05 | ENSMUSG00000038845 | 10.624108 | NaN | 1.249818e-23 | ENSMUSG00000032733 | 8.025130 | 294.125763 | 4.569014e-13 | ENSMUSG00000032852 | 10.792939 | 304.172455 | 5.581918e-25 |
| 39 | ENSMUSG00000074637 | 8.075441 | 150.058151 | 1.655339e-13 | ENSMUSG00000069273 | 8.592979 | 110.775238 | 4.485846e-15 | ENSMUSG00000062444 | 7.906251 | inf | 1.404131e-12 | ENSMUSG00000032657 | 4.704062 | 181.422836 | 5.625127e-04 | ENSMUSG00000031715 | 10.541922 | inf | 2.930263e-23 | ENSMUSG00000024155 | 8.377050 | 251.302048 | 1.222473e-14 | ENSMUSG00000085840 | 9.598436 | 33.624336 | 1.236418e-19 | ENSMUSG00000074578 | 6.192357 | 668.383118 | 1.399139e-07 | ENSMUSG00000032249 | 8.303944 | 133.634857 | 5.331168e-14 | ENSMUSG00000022474 | 7.622097 | NaN | 1.321193e-11 | ENSMUSG00000039329 | 5.372164 | NaN | 1.390757e-05 | ENSMUSG00000063524 | 10.597474 | NaN | 1.620541e-23 | ENSMUSG00000098112 | 8.014524 | 513.933044 | 4.877294e-13 | ENSMUSG00000015980 | 10.779205 | 405.565186 | 6.346268e-25 |
| 40 | ENSMUSG00000039001 | 8.042783 | inf | 2.089343e-13 | ENSMUSG00000021196 | 8.576842 | 640.355835 | 5.035946e-15 | ENSMUSG00000027715 | 7.833958 | inf | 2.441455e-12 | ENSMUSG00000051159 | 4.688323 | 77.850838 | 5.832453e-04 | ENSMUSG00000030884 | 10.528224 | inf | 3.306810e-23 | ENSMUSG00000032065 | 8.371287 | 269.899750 | 1.257033e-14 | ENSMUSG00000054408 | 9.555412 | 484.545410 | 1.809976e-19 | ENSMUSG00000104960 | 6.158553 | 171.430374 | 1.689607e-07 | ENSMUSG00000020812 | 8.296021 | 285.214233 | 5.559838e-14 | ENSMUSG00000028851 | 7.602215 | NaN | 1.503376e-11 | ENSMUSG00000019139 | 5.360795 | NaN | 1.435661e-05 | ENSMUSG00000040463 | 10.582635 | NaN | 1.823246e-23 | ENSMUSG00000069049 | 7.996294 | 169.738083 | 5.540696e-13 | ENSMUSG00000038393 | 10.768087 | inf | 7.062998e-25 |
| 41 | ENSMUSG00000021411 | 7.992451 | 91.574432 | 2.948363e-13 | ENSMUSG00000097772 | 8.545337 | 32.158131 | 6.461185e-15 | ENSMUSG00000023004 | 7.822543 | NaN | 2.609787e-12 | ENSMUSG00000022914 | 4.675897 | inf | 6.075526e-04 | ENSMUSG00000061232 | 10.505014 | inf | 4.129459e-23 | ENSMUSG00000002833 | 8.362066 | inf | 1.331569e-14 | ENSMUSG00000029784 | 9.545214 | 274.806427 | 1.969860e-19 | ENSMUSG00000025508 | 6.144292 | NaN | 1.790225e-07 | ENSMUSG00000060206 | 8.286908 | 192.098740 | 5.859651e-14 | ENSMUSG00000029249 | 7.555411 | inf | 2.105000e-11 | ENSMUSG00000017764 | 5.355307 | inf | 1.453091e-05 | ENSMUSG00000018326 | 10.581874 | inf | 1.823246e-23 | ENSMUSG00000022849 | 7.985024 | 441.039581 | 5.949674e-13 | ENSMUSG00000029322 | 10.721654 | 122.006172 | 1.144675e-24 |
| 42 | ENSMUSG00000031523 | 7.963251 | 85.648529 | 3.658033e-13 | ENSMUSG00000032515 | 8.537269 | 89.259338 | 6.657716e-15 | ENSMUSG00000017861 | 7.810748 | inf | 2.799387e-12 | ENSMUSG00000032431 | 4.655187 | 315.054504 | 6.527386e-04 | ENSMUSG00000063480 | 10.498926 | inf | 4.302173e-23 | ENSMUSG00000020419 | 8.337477 | 339.656342 | 1.591044e-14 | ENSMUSG00000034723 | 9.522587 | 393.600372 | 2.416709e-19 | ENSMUSG00000086290 | 6.091475 | 294.197784 | 2.277741e-07 | ENSMUSG00000020402 | 8.276212 | 384.198639 | 6.261367e-14 | ENSMUSG00000033751 | 7.546712 | 166.680008 | 2.198071e-11 | ENSMUSG00000021848 | 5.354522 | 124.410980 | 1.453091e-05 | ENSMUSG00000074397 | 10.520995 | inf | 3.404565e-23 | ENSMUSG00000073460 | 7.969114 | 395.583954 | 6.635161e-13 | ENSMUSG00000009941 | 10.691896 | inf | 1.547314e-24 |
| 43 | ENSMUSG00000050856 | 7.926750 | 158.263229 | 4.716048e-13 | ENSMUSG00000035203 | 8.536500 | 124.810936 | 6.657716e-15 | ENSMUSG00000054405 | 7.773459 | inf | 3.675188e-12 | ENSMUSG00000016637 | 4.601341 | 160.203964 | 7.729063e-04 | ENSMUSG00000017707 | 10.491697 | inf | 4.538887e-23 | ENSMUSG00000040841 | 8.329023 | 40.737133 | 1.675661e-14 | ENSMUSG00000024155 | 9.485300 | 160.356232 | 3.434562e-19 | ENSMUSG00000040952 | 6.077214 | NaN | 2.397540e-07 | ENSMUSG00000045817 | 8.270666 | 105.442490 | 6.410567e-14 | ENSMUSG00000036371 | 7.516476 | NaN | 2.707984e-11 | ENSMUSG00000034341 | 5.323551 | 310.810059 | 1.651907e-05 | ENSMUSG00000006998 | 10.507678 | NaN | 3.832028e-23 | ENSMUSG00000000253 | 7.955525 | 272.290985 | 7.263240e-13 | ENSMUSG00000044562 | 10.690262 | 262.991150 | 1.564542e-24 |
| 44 | ENSMUSG00000020962 | 7.917529 | inf | 5.029395e-13 | ENSMUSG00000005672 | 8.509989 | inf | 8.006979e-15 | ENSMUSG00000034330 | 7.761283 | inf | 3.955965e-12 | ENSMUSG00000041859 | 4.588915 | NaN | 8.102741e-04 | ENSMUSG00000067702 | 10.444516 | NaN | 7.304814e-23 | ENSMUSG00000067702 | 8.329023 | NaN | 1.675661e-14 | ENSMUSG00000098358 | 9.468409 | 262.101990 | 4.010905e-19 | ENSMUSG00000050856 | 6.059784 | inf | 2.600103e-07 | ENSMUSG00000020186 | 8.266308 | 492.115753 | 6.501414e-14 | ENSMUSG00000047945 | 7.494109 | inf | 3.121426e-11 | ENSMUSG00000009894 | 5.302381 | inf | 1.779585e-05 | ENSMUSG00000076617 | 10.465062 | inf | 5.881387e-23 | ENSMUSG00000096255 | 7.945250 | 146.663925 | 7.742485e-13 | ENSMUSG00000006457 | 10.652984 | 621.815491 | 2.248756e-24 |
| 45 | ENSMUSG00000105039 | 7.900623 | 38.950268 | 5.667483e-13 | ENSMUSG00000099517 | 8.505379 | 67.615089 | 8.154424e-15 | ENSMUSG00000020592 | 7.706493 | 738.891907 | 5.952776e-12 | ENSMUSG00000002416 | 4.584773 | 39.036774 | 8.102741e-04 | ENSMUSG00000025630 | 10.435384 | inf | 7.762125e-23 | ENSMUSG00000026277 | 8.318266 | NaN | 1.817296e-14 | ENSMUSG00000052616 | 9.453749 | 240.039642 | 4.553914e-19 | ENSMUSG00000071415 | 6.041297 | NaN | 2.890246e-07 | ENSMUSG00000025967 | 8.258385 | NaN | 6.796706e-14 | ENSMUSG00000040681 | 7.492038 | NaN | 3.121426e-11 | ENSMUSG00000028378 | 5.289051 | 210.774719 | 1.886519e-05 | ENSMUSG00000025393 | 10.455931 | NaN | 6.335664e-23 | ENSMUSG00000058328 | 7.912105 | 136.457794 | 9.923300e-13 | ENSMUSG00000025202 | 10.642193 | 173.063492 | 2.463111e-24 |
| 46 | ENSMUSG00000058013 | 7.895628 | 308.003204 | 5.824983e-13 | ENSMUSG00000015023 | 8.500000 | 81.550468 | 8.363354e-15 | ENSMUSG00000018446 | 7.697742 | NaN | 6.239226e-12 | ENSMUSG00000055817 | 4.549566 | 288.457764 | 9.254022e-04 | ENSMUSG00000027722 | 10.434623 | inf | 7.762125e-23 | ENSMUSG00000046338 | 8.263323 | NaN | 2.786526e-14 | ENSMUSG00000071470 | 9.430484 | 425.172455 | 5.576280e-19 | ENSMUSG00000060143 | 6.024396 | NaN | 3.149376e-07 | ENSMUSG00000029094 | 8.247688 | 268.077942 | 7.275190e-14 | ENSMUSG00000024217 | 7.488310 | 156.794952 | 3.143029e-11 | ENSMUSG00000015869 | 5.276898 | NaN | 2.001462e-05 | ENSMUSG00000028410 | 10.434623 | NaN | 7.692305e-23 | ENSMUSG00000021179 | 7.858409 | 525.415283 | 1.396201e-12 | ENSMUSG00000074665 | 10.623554 | 190.167221 | 2.936125e-24 |
| 47 | ENSMUSG00000025362 | 7.876802 | NaN | 6.646391e-13 | ENSMUSG00000020257 | 8.460810 | 239.072983 | 1.147405e-14 | ENSMUSG00000070837 | 7.659692 | 285.264893 | 8.221657e-12 | ENSMUSG00000020385 | 4.545010 | 196.358841 | 9.323482e-04 | ENSMUSG00000055943 | 10.426252 | inf | 8.066317e-23 | ENSMUSG00000029705 | 8.262938 | 353.626495 | 2.786526e-14 | ENSMUSG00000038695 | 9.421880 | 392.144379 | 5.975719e-19 | ENSMUSG00000046364 | 6.021755 | NaN | 3.149376e-07 | ENSMUSG00000097772 | 8.222334 | 49.423809 | 8.803980e-14 | ENSMUSG00000020027 | 7.482512 | inf | 3.216484e-11 | ENSMUSG00000028525 | 5.225932 | 140.385574 | 2.446018e-05 | ENSMUSG00000026234 | 10.433481 | NaN | 7.692305e-23 | ENSMUSG00000036912 | 7.852112 | 626.760315 | 1.431318e-12 | ENSMUSG00000054003 | 10.619957 | inf | 3.033326e-24 |
| 48 | ENSMUSG00000091021 | 7.873728 | 31.908327 | 6.748745e-13 | ENSMUSG00000036155 | 8.444288 | 69.887093 | 1.295346e-14 | ENSMUSG00000021806 | 7.651321 | inf | 8.595978e-12 | ENSMUSG00000079435 | 4.544595 | NaN | 9.323482e-04 | ENSMUSG00000022174 | 10.425872 | NaN | 8.066317e-23 | ENSMUSG00000051977 | 8.241423 | 700.015625 | 3.188751e-14 | ENSMUSG00000000823 | 9.399570 | 405.061432 | 7.205221e-19 | ENSMUSG00000022048 | 6.013832 | 391.824829 | 3.251249e-07 | ENSMUSG00000024754 | 8.202526 | 443.248291 | 1.000111e-13 | ENSMUSG00000047751 | 7.479198 | NaN | 3.231307e-11 | ENSMUSG00000062444 | 5.160853 | inf | 3.233007e-05 | ENSMUSG00000028691 | 10.426252 | NaN | 8.130995e-23 | ENSMUSG00000022487 | 7.847471 | 593.196228 | 1.438109e-12 | ENSMUSG00000036880 | 10.609493 | inf | 3.333429e-24 |
| 49 | ENSMUSG00000030432 | 7.870270 | NaN | 6.874281e-13 | ENSMUSG00000086290 | 8.441983 | 169.743683 | 1.295346e-14 | ENSMUSG00000001525 | 7.617838 | NaN | 1.092408e-11 | ENSMUSG00000031591 | 4.533412 | 262.582214 | 9.660941e-04 | ENSMUSG00000026021 | 10.423969 | inf | 8.066317e-23 | ENSMUSG00000038187 | 8.217985 | inf | 3.663352e-14 | ENSMUSG00000062170 | 9.398296 | 578.268677 | 7.248019e-19 | ENSMUSG00000032330 | 5.982670 | NaN | 3.772696e-07 | ENSMUSG00000046434 | 8.202130 | NaN | 1.000111e-13 | ENSMUSG00000028832 | 7.469671 | inf | 3.380111e-11 | ENSMUSG00000046879 | 5.155365 | 180.239044 | 3.288309e-05 | ENSMUSG00000021273 | 10.417120 | inf | 8.771522e-23 | ENSMUSG00000019139 | 7.810349 | NaN | 1.857775e-12 | ENSMUSG00000028150 | 10.502892 | 299.704712 | 9.673866e-24 |
DE_female_E105 = sc.get.rank_genes_groups_df(gcAnndata, group='female_E10.5', pval_cutoff=0.05, log2fc_min=1.588, key="rank_genes_groups", gene_symbols="external_gene_name").sort_values(by=["scores"], ascending=[False])
DE_female_E115 = sc.get.rank_genes_groups_df(gcAnndata, group='female_E11.5', pval_cutoff=0.05, log2fc_min=1.588, key="rank_genes_groups", gene_symbols="external_gene_name").sort_values(by=["scores"], ascending=[False])
DE_female_E125 = sc.get.rank_genes_groups_df(gcAnndata, group='female_E12.5', pval_cutoff=0.05, log2fc_min=1.588, key="rank_genes_groups", gene_symbols="external_gene_name").sort_values(by=["scores"], ascending=[False])
DE_female_E135 = sc.get.rank_genes_groups_df(gcAnndata, group='female_E13.5', pval_cutoff=0.05, log2fc_min=1.588, key="rank_genes_groups", gene_symbols="external_gene_name").sort_values(by=["scores"], ascending=[False])
DE_female_E145 = sc.get.rank_genes_groups_df(gcAnndata, group='female_E14.5', pval_cutoff=0.05, log2fc_min=1.588, key="rank_genes_groups", gene_symbols="external_gene_name").sort_values(by=["scores"], ascending=[False])
DE_female_E155 = sc.get.rank_genes_groups_df(gcAnndata, group='female_E15.5', pval_cutoff=0.05, log2fc_min=1.588, key="rank_genes_groups", gene_symbols="external_gene_name").sort_values(by=["scores"], ascending=[False])
DE_female_E165 = sc.get.rank_genes_groups_df(gcAnndata, group='female_E16.5', pval_cutoff=0.05, log2fc_min=1.588, key="rank_genes_groups", gene_symbols="external_gene_name").sort_values(by=["scores"], ascending=[False])
sc.tl.rank_genes_groups(gcAnndata, groupby="stage_sex", groups=["female_E16.5"], reference = "female_E13.5", method='wilcoxon', tie_correct=True, key_added="female", pts=True)
DE_female_E165vE135 = sc.get.rank_genes_groups_df(gcAnndata, group="female_E16.5", key="female", pval_cutoff=0.05, log2fc_min=1.588, gene_symbols="external_gene_name")
ranking genes
WARNING: It seems you use rank_genes_groups on the raw count data. Please logarithmize your data before calling rank_genes_groups.
finished: added to `.uns['female']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
#DE_female_E165vE135 = sc.get.rank_genes_groups_df(gcAnndata,
# group="female_E16.5",
# key="female",
# pval_cutoff=0.01,
# log2fc_min=2,
# gene_symbols="external_gene_name").sort_values(by=["scores"], ascending=[False]).dropna(axis=0)#["external_gene_name"].to_list())
DE_female_E165vE135[DE_female_E165vE135.pvals_adj<0.05]
| names | scores | logfoldchanges | pvals | pvals_adj | external_gene_name | pct_nz_group | |
|---|---|---|---|---|---|---|---|
| 0 | ENSMUSG00000031397 | 8.812840 | inf | 1.220150e-18 | 3.690778e-15 | Tktl1 | 1.000000 |
| 1 | ENSMUSG00000096210 | 8.531160 | inf | 1.448876e-17 | 2.788955e-14 | H1f0 | 0.985915 |
| 2 | ENSMUSG00000009596 | 8.432611 | inf | 3.380348e-17 | 4.771699e-14 | Taf7l | 1.000000 |
| 3 | ENSMUSG00000051592 | 8.376738 | 763.262146 | 5.441513e-17 | 6.064137e-14 | Ccnb3 | 0.957746 |
| 4 | ENSMUSG00000031432 | 8.178836 | inf | 2.865999e-16 | 2.758394e-13 | Prps1 | 0.943662 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 1646 | ENSMUSG00000032913 | 2.639403 | 72.368431 | 8.305215e-03 | 4.984542e-02 | Lrig2 | 0.788732 |
| 1647 | ENSMUSG00000024521 | 2.638535 | 39.953098 | 8.326498e-03 | 4.995899e-02 | Pmaip1 | 0.267606 |
| 1648 | ENSMUSG00000026005 | 2.638428 | 49.923851 | 8.329134e-03 | 4.996064e-02 | Rpe | 0.605634 |
| 1649 | ENSMUSG00000045382 | -2.687850 | 2.531237 | 7.191361e-03 | 4.471949e-02 | Cxcr4 | 0.070423 |
| 1650 | ENSMUSG00000032702 | -2.930349 | 6.624206 | 3.385819e-03 | 2.554019e-02 | Kank1 | 0.309859 |
1651 rows × 7 columns
gcAnndata.write_h5ad("/users/k2142342/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_17Jul23//gcAnndata_reproduce.h5ad")
sc.tl.rank_genes_groups(gcAnndata, 'stage_sex', method='wilcoxon')
ranking genes
WARNING: It seems you use rank_genes_groups on the raw count data. Please logarithmize your data before calling rank_genes_groups.
finished: added to `.uns['rank_genes_groups']`
'names', sorted np.recarray to be indexed by group ids
'scores', sorted np.recarray to be indexed by group ids
'logfoldchanges', sorted np.recarray to be indexed by group ids
'pvals', sorted np.recarray to be indexed by group ids
'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:01)
sc.pl.rank_genes_groups(gcAnndata, n_genes=25, sharey=False, gene_symbols='external_gene_name',)
#sc.get.rank_genes_groups_df(gcAnndata, group='male_E12.5',
# pval_cutoff=0.00001,
# log2fc_min=1.5,
# gene_symbols="external_gene_name",
# key="rank_genes_groups").sort_values(by=["scores"],
# ascending=[False]).dropna(axis=0)["external_gene_name"].to_list()
gslist = {}
for i in ['male_E10.5', 'female_E10.5', 'male_E11.5', 'female_E11.5', 'male_E12.5', 'female_E12.5', 'male_E13.5', 'female_E13.5', 'male_E14.5', 'female_E14.5', 'male_E15.5', 'female_E15.5', 'male_E16.5', 'female_E16.5']:
genes = sc.get.rank_genes_groups_df(gcAnndata,
group = i,
pval_cutoff=0.01,
log2fc_min=2,
gene_symbols="external_gene_name",
key="rank_genes_groups").sort_values(by=["scores"],
ascending=[False]).dropna(axis=0)["external_gene_name"].to_list()
gslist[i] = genes
top10 = pd.Series(gslist['male_E11.5'][:10] + gslist['male_E12.5'][:10] + gslist['male_E13.5'][:10] + gslist['male_E14.5'][:10] + gslist['male_E15.5'][:10] + gslist['male_E16.5'][:10] + gslist['female_E11.5'][:10] + gslist['female_E12.5'][:10] + gslist['female_E13.5'][:10] + gslist['female_E14.5'][:10] + gslist['female_E15.5'][:10] + gslist['female_E16.5'][:10], name = 'top10')
top10.drop_duplicates()
0 H4c14
1 Abhd2
2 Tbc1d13
3 H2ac15
4 Uty
...
115 Ccnb3
116 Sycp1
117 1700029J07Rik
118 Sycp3
119 Aspa
Name: top10, Length: 110, dtype: object
sns.set_context("paper", font_scale=1, rc={"font.size":7,"axes.labelsize":7,"axes.titlesize":14})
d = sc.pl.matrixplot(gcAnndata,
top10.drop_duplicates(),
cmap = 'Reds',
groupby = "stage_sex",
standard_scale="var",
gene_symbols='external_gene_name',
edgecolor="white",
linewidths=-0,
#swap_axes = True,
figsize = [11,2],
#save = "male_female_top10_horiz.pdf",
)